JP2004102551A - Image processor, image processing method, image reader equipped with that, image forming apparatus, program, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing method, an image processor, an image reader, an image forming apparatus, a program, and a recording medium which can determine the number of dot lines of a manuscript image with a simple constitution and can reproduce a satisfactory output image. <P>SOLUTION: By calculating the number of pixels of dot area, it is determined whether the manuscript image includes a dot or not by a first dot determination part 8, then the number of dot lines is determined by a number-of-dot-lines determination part 9 if it is judged that the dot is included. The number-of-dot-lines determination part 9 determines the number of dot lines according to proportion of the number of dot pixels, which is calculated in the number-of-dot-lines determination part 9, to the number of pixels of a dot area calculated in the first dot determination part 8. A control part 17 controls the number-of-dot-lines determination part 9 to determine the number of dot lines only if it is judged that the manuscript image includes the dot by the first dot determination part 8. <P>COPYRIGHT: (C)2004,JPO

Description

【０１２９】
このように、第１判定部２０ａでは、表１に示される領域判定信号からの判定例のように、各画素それぞれどの領域の画素であるか判定される（ここでは、網点領域画素に対して優先順位を高く設定しているので、文字領域画素判定：１、網点領域画素判定：１の時は網点画素であると判定するようにしている。）。
【０１３０】
こうして第１判定部２０ａにて判定された各画素を、文字領域画素カウンタ２２、網点領域画素カウンタ２３ａにてそれぞれカウントアップする。更に、除算器２５にて総画素数に対する網点画素数の比を算出する。第２判定部２６では、当該総画素数に対する網点画素数の比が所定値（例えば、０．０５）より大きければ原稿画像中に網点ありとし、所定値以下であれば原稿画像中に網点なしという判定信号が制御部１７に出力される。
【０１３１】
（２）網点線数判定部
次に、網点線数判定部９について説明する。網点線数判定部９ａは、図７に示されるように、第２網点領域判定部３４、網点領域画素カウンタ２３ｂ、網点線数判別部３５、閾値設定部３６を備えている。また、制御部１７は、後述するパラメータ設定部３１ｂのパラメータを、前述した第１網点判定部８の判定結果に応じて変更する。
【０１３２】
第２網点領域判定部３４は、２値化処理部２７、反転回数計数部２８、最大値選択部２９、反転回数判定部３０、およびパラメータ設定部３１ｂとを備えている。すなわち、第２網点領域判定部３４の構成は、図５に示される第１網点領域判定部１９ａの構成と同様である。また、第２網点領域判定部３４は、第１網点領域判定部１９ａと同様に、注目画素が網点領域画素であるか否かを判定する。
【０１３３】
パラメータ設定部３１ｂには、第２網点領域判定部３４に対する処理パラメータ（第２の閾値）が格納されている。このパラメータにより、注目画素が網点領域画素であるか非網点領域画素であるかの判定がなされる。なお、このパラメータは、前述した第１網点判定部８の判定結果に応じて制御部１７により変更される。例えば、第１網点判定部８にて原稿画像中に網点有りと判定された場合、制御部１７は、パラメータ設定部３１ｂのパラメータを変更する。この場合、パラメータ設定部３１ｂの変更前のパラメータを「２」、変更後のパラメータを「４」のように、変更前より大きい値に設定する。これにより、反転回数が少ない（すなわち低線数網点である）と、網点領域画素と判定しないようにすることができる。
【０１３４】
網点線数判別部３５は、第１網点判定部８の網点領域画素カウンタ２３ａにて算出した第１網点領域画素数（ｓｃｒ１）と、網点線数判定部９ａの網点領域画素カウンタ２３ｂにて算出した第２網点領域画素数（ｓｃｒ２）との比を閾値設定部３６に設定された閾値（ＴＨ２）と比較することにより、網点線数を判別する。
【０１３５】
なお、第１網点判定部８の網点領域判定部１９ａと、第２網点領域判定部３４とは、共通とする構成でもよい。また、図４の網点領域画素カウンタ２３ａと図７の網点領域画素カウンタ２３ｂも共通とする構成としてもよい。この場合、制御部１７は、第１網点判定部８にて原稿画像に網点領域画素があると判定された時に、図５のパラメータ設定部３１ａのパラメータを変更するようにすればよい。
【０１３６】
次に、網点線数判定部９の別の構成について説明する。図８に示されるように、網点線数判定部９ｂは、文字領域判定部１８、第２網点領域判定部３４、第１判定部２０ｂ、文字領域画素カウンタ２２、網点領域画素カウンタ２３ｃ、網点線数判別部３５、および閾値設定部３６を備えている。図８の網点線数判定部９ｂは、図４に示される第１網点判定部８の構成、および図７網点線数判定部９ａと類似の構成である。しかし、網点線数判定部９ｂは、図４の第１網点判定部８とは異なり、総画素数カウンタ２４、除算器２５、および第２判定部２５を備えず、網点線数判別部３５および閾値設定部３６を備えている。また、網点線数判定部９ｂは、図７の網点線数判定部９ａとは異なり、第２網点領域判定部３４に加えて、文字領域判定部１８、第１判定部２０ｂおよび文字領域画素カウンタ２２を備えている。制御部１７は、網点線数判定部９ａと同様に、第２網点領域判定部３４のパラメータを変更するように設定されている。
【０１３７】
網点線数判定部９ｂに入力されるシェーディング補正されたＲＧＢ信号は、文字領域判定部１８にて、注目画素が文字領域画素であるか否か、また第２網点領域判定部３４にて、注目画素が網点領域画素であるか否かが判定される。なお、第２網点領域判定部３４の構成は、図７で説明した第２網点領域判定部３４の構成と同様でよい。
【０１３８】
これらの判定結果から、第１判定部２０ｂにて、例えば、前述した表１に示される判定例のように、各画素それぞれどの領域の画素であるか判定される。こうして判定された各画素は、文字領域画素カウンタ２２および網点領域画素カウンタ２３ｃにてそれぞれカウントアップされる。網点線数判別部３５では、第１網点判定部８にて算出した網点領域画素数と、第２網点領域判定部３４にて算出した網点領域画素数との比を閾値設定部３６の閾値と比較することにより、網点線数を判別する。
【０１３９】
以上説明した、第１網点判定部８、網点線数判定部９、および制御部１７の画像処理（特に、網点線数判定部９での画像処理）は、例えば、図９に示されるフローチャートのように行われる。すなわち、まず、本スキャンに先立ってプレスキャンが行われる（Ｓ１３）。次に、第１網点判定部８にて、得られた入力信号データから、第１網点領域画素数（ｓｃｒ１）の算出および網点領域画素の有無が判定される（Ｓ１４、Ｓ１５）。このとき、原稿画像に網点が無いと判定されると、網点線数判定部９にて網点線数の判定は行わず（Ｓ１６）、処理を終了して本スキャン処理に入る。一方、原稿画像に網点があると判定されると、制御部１７にて網点線数判定部９のパラメータ設定部３１のパラメータ（第２の閾値）が変更される（Ｓ１７）。続いて、変更されたパラメータ（第２の閾値）を用いて、第２網点領域判定部３４および網点領域画素カウンタ２３にて第２網点領域画素数（ｓｃｒ２）が算出される（Ｓ１８）。次に、網点線数判別部３５にて、第１および第２網点領域画素数（ｓｃｒ１およびｓｃｒ２）の比（すなわち、第２網点領域画素数／第１網点領域画素数：ｓｃｒ２／ｓｃｒ１）が算出される（Ｓ１９）。次に、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）と、網点線数判定部９の閾値設定部３６に設定される閾値（ＴＨ２：例えば、０．２５）（閾値の例としては、後述の図１０を参照。）とが比較される（Ｓ２０）。そして、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が閾値（ＴＨ２）より大きければ高線数の網点であると判定され（Ｓ２１）、本スキャン処理に入る。一方、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が、閾値（ＴＨ２）以下であれば低線数の網点であると判定され（Ｓ２２）、本スキャン処理に入る。
【０１４０】
本スキャン以降の処理は、網点線数判定結果に応じて、後段の領域分離パラメータ・フィルタ係数・ディザパターンなどが選択され、最適な処理が行われる。これにより、網点線数に応じた各処理がなされるため、鮮鋭度を保ちながらモアレのない出力画像を得ることが可能となる。なお、本スキャンは、上記Ｓ１３〜Ｓ２２までに得られた原稿画像の情報をもとに最適な画像処理を行うためのスキャンである。
【０１４１】
このように、網点線数判定部９では、低線数の網点は反転回数が少なく、高線数の網点は反転回数が多いという特徴を利用して画像処理が行われる。それゆえ、従来のようにパターンマッチングや、複雑な変換処理をすることなく、簡単な構成にて網点線数の判定を行うことが可能となる。
【０１４２】
また、図９のフローチャートに示される画像処理は、例えば、図１０に示されるフローチャートのように行うこともできる。図１０のフローチャートは、網点線数判定部９の閾値設定部３６に複数の閾値（ＴＨ３〜ＴＨ５）を備えている例である。図１０に示されるフローチャートにおけるＳ２３〜Ｓ２９は、図９に示されるフローチャートのＳ１３〜Ｓ１９に相当するので説明を省略する。図１０のフローチャートにおいて、網点線数判別部３５にて、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が算出される（Ｓ２９）と、次に、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）と、網点線数判定部９の閾値設定部３６に設定される閾値（ＴＨ３：前述の閾値（ＴＨ２）とは異なる）とが比較される（Ｓ３０）。そして、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が閾値（ＴＨ３）より大きければ高線数の網点と判定され（Ｓ３１）、本スキャン処理に入る。また、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が第３の閾値（ＴＨ３）以下であれば、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）と閾値設定部３６に設定される閾値（ＴＨ４）とが比較される（Ｓ３２）。そして、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が閾値（ＴＨ４）より大きければ、高線数よりの中線数の網点と判定され（Ｓ３３）、本スキャンに入る。また、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が閾値（ＴＨ４）以下であれば、更に、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）と、閾値設定部３６に設定されるの閾値（ＴＨ５）とが比較される（Ｓ３４）。そして、第１および第２網点領域画素数の比（ｓｃｒ２／ｓｃｒ１）が閾値（ＴＨ５）より大きければ低線数よりの中線数の網点と判定され（Ｓ３５）、閾値（ＴＨ５）以下であれば低線数の網点であると判定され（Ｓ３６）、本スキャン処理に入る。
【０１４３】
閾値（ＴＨ３）、閾値（ＴＨ４）、および閾値（ＴＨ５）の閾値は、ＴＨ３＞ＴＨ４＞ＴＨ５の条件を満たしていれば、特に限定されるものではない。例えば、ＴＨ３＝０．４０、ＴＨ４＝０．２５、ＴＨ５＝０．１０とすればよい。
【０１４４】
また、本スキャン以降の処理は、網点線数判定結果に応じて、領域分離パラメータ・フィルタ係数・ディザパターンなどが選択され、最適な処理が行われる。これにより、網点線数に応じた各処理がなされるため、鮮鋭度を保ちながらモアレのない出力画像を得ることが可能となる。
【０１４５】
さらに、網点線数判定部９における閾値設定部３５の閾値を複数設定すれば、原稿画像の判定結果を段階的に出力することが可能となる。したがって、当該閾値を多く設定すればするほど、細かい判定結果をえることが可能である。その結果、より原稿画像の特徴に応じた設定にて画像処理を行うことが可能となり、高画質化が図れる。
【０１４６】
なお、当然のことながら、本発明の画像処理は、上記の処理に限定されるものでない。図１０は、プレスキャンを行う構成にて記載したが、プレスキャンを行わない構成であってもよい。この場合、入力画像データを画像メモリなどの記憶手段に格納しておき、この画像データを用いて網点線数判定処理を行った後、一連の画像処理を行うよう構成すればよい。
【０１４７】
次に、反転回数計数部２８について説明する。反転回数計数部２８は、図１１に示されるように、主走査方向計数部３２および副走査方向計数部３３に加えて、斜め方向計数部３７ａを備えている。
【０１４８】
反転回数計数部２８は、２値化画像から反転回数を、主走査方向、副走査方向、斜め方向の各方向に算出する。なお、図中破線で示したように、ｎ本（ｎは任意の自然数）の斜め方向計数部３７ａ、３７ｂ、…３７ｎを備えて、複数の角度に渡り（複数の方向に）反転回数を算出するような構成であってもよい。これにより、原稿画像がスクリーン角度を有する網点の場合、または原稿画像の設置方向に関わらず、反転回数を計数できる。それゆえ、精度よく網点線数を判定することができる。
【０１４９】
図１２（ａ）〜図１２（ｄ）および図１３（ａ）〜図１３（ｄ）には、反転回数計数部２８における、各方向の反転回数計数の例が示される。なお、図１２および図１３において、注目画素を含むブロック画素サイズ（マスクサイズという場合もある）は１０×１０としている。
【０１５０】
図１２（ａ）は、主走査方向計数部３２の計数方式の例である。ａ１、ａ２、ａ３、………、ａ１０の反転回数計数結果（Ｈｒｅｖ１）、ｂ１、ｂ２、ｂ３、………、ｂ１０の反転回数計数結果（Ｈｒｅｖ２）というふうに、順次、走査方向に反転回数を計数し、走査方向の反転回数計数（Ｈｒｅｖ１〜Ｈｒｅｖ１０）を得る。
【０１５１】
図１２（ｂ）は、副走査方向計数部３３の計数方式の例である。この場合も、主走査方向計数部３２の場合と同様に、ａ１、ｂ１、ｃ１、………、ｊ１の反転回数計数結果（Ｖｒｅｖ１）、ａ２、ｂ２、ｃ２、………、ｊ２の反転回数計数結果（Ｖｒｅｖ２）というふうに、順次、副走査方向に反転回数を計数し、副走査方向の反転回数計数（Ｖｒｅｖ１〜Ｖｒｅｖ１０）を得る。
【０１５２】
図１２（ｃ）、図１２（ｄ）、および図１３（ａ）〜図１３（ｄ）は、斜め方向計数部３７の計数方式の例である。図１２（ｃ）には、斜め４５°方向の計数方式の例が示される。ａ３、ｂ４、ｃ５、………、ｈ１０の反転回数計数結果（Ｓ１ｒｅｖ１）、ａ２、ｂ３、ｃ４、………、ｉ１０の反転回数計数結果（Ｓ１ｒｅｖ２）、ａ１、ｂ２、ｃ３、………、ｊ１０の反転回数計数結果（Ｓ１ｒｅｖ３）というふうに、順次、斜め４５°方向の反転回数を計数し、斜め４５°方向の反転回数計数（Ｓ１ｒｅｖ１〜Ｓ１ｒｅｖ５）を得る。
【０１５３】
なお、斜め方向では、１ラインの反転回数計数対象画素数が、Ｓ１ｒｅｖ１では８画素、Ｓ１ｒｅｖ２では９画素、Ｓ１ｒｅｖ３では１０画素と異なるので、計数結果を補正してもよい。例えば、下記式（２）および式（３）のように補正することができる。なお、この補正は、図示しない補正部により行えばよい。
Ｓ１ｒｅｖ１’＝　Ｓ１ｒｅｖ１　×　１０　／　８　　・・・（２）
Ｓ１ｒｅｖ２’＝　Ｓ１ｒｅｖ２　×　１０　／　９　　・・・（３）
これにより、全ての方向に計数された反転回数計数対象画素数は、同一条件となる。すなわち、反転回数を計数する各方向での反転回数の誤差が解消される。
【０１５４】
図１２（ｄ）には、斜め−４５°方向の計数方式の例が示される。ａ８、ｂ７、ｃ６、………、ｈ１の反転回数計数結果（Ｓ２ｒｅｖ１）、ａ９、ｂ８、ｃ７、………、ｉ１の反転回数計数結果（Ｓ２ｒｅｖ２）、ａ１０、ｂ９、ｃ８、　………、ｊ１の反転回数計数結果（Ｓ２ｒｅｖ３）というふうに、順次、斜め−４５°方向の判定回数を計数し、斜め−４５°方向の反転回数計数（Ｓ２ｒｅｖ１〜Ｓ２ｒｅｖ５）を得る。この場合の反転回数計数結果も、前述の斜め４５°の場合（Ｓ１ｒｅｖ１’〜Ｓ１ｒｅｖ５’）と同様に、例えば、式（２）および式（３）のように補正してもよい。
【０１５５】
図１３（ａ）には、斜め約７２°方向の計数方式の例が示される。ａ１、ａ２、ａ３、ｂ４、………、ｄ１０の反転回数計数結果（Ｓ３ｒｅｖ１）、ｂ１、ｂ２、ｂ３、ｃ４、………、ｅ１０の反転回数計数結果（Ｓ３ｒｅｖ２）というふうに、順次、斜め７２°方向の反転回数を計数し、斜め７２°方向の反転回数計数（Ｓ３ｒｅｖ１〜Ｓ３ｒｅｖ７）を得る。
【０１５６】
図１３（ｂ）には、斜め約−７２°方向の計数方式の例が示される。この場合も図１３（ａ）の場合と同様に、ａ１０、ａ９、ａ８、ｂ７、………、ｄ１の反転回数計数結果（Ｓ４ｒｅｖ１）、ｂ１０、ｂ９、ｂ８、ｃ７、………、ｅ１の反転回数計数結果（Ｓ４ｒｅｖ２）というふうに、順次、斜め−７２°方向の反転回数を計数し、斜め−７２°方向の反転回数計数（Ｓ４ｒｅｖ１〜Ｓ４ｒｅｖ７）を得る。
【０１５７】
図１３（ｃ）には、斜め約−２２°方向の計数方式が示される。ａ１０、ｂ１０、ｃ１０、ｄ９、………、ｊ７の反転回数計数結果（Ｓ５ｒｅｖ１）、ａ９、ｂ９、ｃ９、ｄ８、………、ｊ６の反転回数計数結果（Ｓ５ｒｅｖ２）というふうに、順次、斜め約−２２°方向の反転回数を計数し、斜め約−２２°方向の反転回数計数（Ｓ５ｒｅｖ１〜Ｓ５ｒｅｖ７）を得る。
【０１５８】
図１３（ｄ）には、斜め約６３°方向の計数方式が示される。ａ１、ａ２、ｂ３、ｂ４、………、ｅ１０の反転回数計数結果（Ｓ６ｒｅｖ１）、ｂ１、ｂ２、ｃ３、ｃ４、………、ｆ１０の反転回数計数結果（Ｓ６ｒｅｖ２）というふうに、順次、斜め約６３°方向の反転回数を計数し、斜め約６３°方向の反転回数計数（Ｓ６ｒｅｖ１〜Ｓ６ｒｅｖ６）を得る。
【０１５９】
上記図１２および図１３の例のように、斜め方向に関しては、任意の角度での反転回数を計数するよう設定しておく。どの角度の方向の計数を行うかは、組み合わせを予め設定しておいてもよいし、任意に選択できるよう構成してもよい。組み合わせを選択する際には、デジタルカラー複写機１に備えられる操作パネル５より入力できるようにすればよい。また、本発明を後述するコンピュータシステムを用いて実現する際には、液晶ディスプレイなどの画像表示装置に組み合わせのパターンを表示し、キーボードやマウスを用いて所望のパターンを選択するようにすれば良い。
【０１６０】
このようにして算出された、主走査方向、副走査方向、および斜め方向の各方向での反転回数計数結果の中から、最大となる値が最大値選択部にて選択され、反転回数判定部にてパラメータと比較される。
【０１６１】
次に、図１２および図１３に示されるような反転計数方式による、網点パターンと反転回数計数の例を、図１４および図１５を用いて説明する。図１４（ａ）、図１４（ｂ）、図１５（ａ）、図１５（ｂ）において、反転回数計数方向は、図１２および図１３の方向、すなわち、主走査方向（図１２（ａ））、副走査方向（図１２（ｂ））、斜め±４５°（図１２（ｃ）、図１２（ｄ））、斜め約±７２°（図１３（ａ）、図１３（ｂ））とする。
【０１６２】
図１４（ａ）の上段には、マスクサイズが１０×１０における高線数の網点の例が、下段には上段の高線数の網点における各方向の反転回数の表がそれぞれ示される。この場合の反転回数の最大値は、図中上段の矢印方向および下段の表の斜線部で示される、斜め４５°方向（Ｓ１ｒｅｖ３）の“９”となる。
【０１６３】
同様に、図１４（ｂ）には、低線数の網点の例と、その各方向反転回数とが示される。この場合の反転回数の最大値は、図中上段の矢印方向および下段の表の斜線部で示される、斜め±４５°方向（Ｓ１ｒｅｖ２〜Ｓ１ｒｅｖ４、Ｓ２ｒｅｖ２〜Ｓ２ｒｅｖ４）の“４”となる。
【０１６４】
図１４（ａ）および図１４（ｂ）において、例えば、図７のパラメータ設定部４１の閾値（第２の閾値）を“４”とすれば、第２網点領域判定部３４反転回数の最大値が“９”である図１４（ａ）は網点であると判定され、反転回数の最大値が“４”である図１４（ｂ）は網点でないと判定される。
【０１６５】
図１５（ａ）には、高線数の網点の例と、その各方向反転回数とが示される。この場合の反転回数の最大値は、図中上段の矢印方向および下段の表の斜線部で示される、斜め約７２°方向（Ｓ３ｒｅｖ１、Ｓ３ｒｅｖ５）の“６”となる。
【０１６６】
また、図１５（ｂ）には、低線数網点例と、その各方向反転回数とが示される。この場合の反転回数の最大値は、図中上段の矢印方向および下段の表の斜線部で示される、斜め約７２°方向（Ｓ３ｒｅｖ１、Ｓ３ｒｅｖ２）の“３”となる。
【０１６７】
図１５（ａ）および図１５（ｂ）の場合、主走査方向および副走査方向のみの反転回数の計数結果では、いずれも最大値が“２”となる。このため、高線数、低線数での反転回数の差が検出できない。しかし、斜め方向の反転回数の計数を行えば、こうした場合にも反転回数の差を検出することが可能となる。すなわち、図１４（ａ）および図１４（ｂ）の場合と同様に、図７のパラメータ設定部３１の閾値が“４”であるとすれば、第２網点線数判定部３４にて、反転回数の最大値が“６”である図１５（ａ）は網点であると判定され、反転回数の最大値が“３”である図１５（ｂ）は網点でないと判定される。
【０１６８】
このように、反転回数の計数を、主走査方向、副走査方向のみでなく、任意の角度の方向に対しても行うことにより、原稿画像がスクリーン角度をもつ網点であった場合や、原稿画像が斜めに載置された場合にも、精度良く網点線数の検出を行うことが可能となる。
【０１６９】
なお、第１網点領域判定部、第２網点領域判定部ともこの構成としてもよく、また、第１網点領域判定部、第２網点領域判定部を共通としてもよい。
【０１７０】
ところで、反転回数は、スキャン速度すなわち副走査方向の倍率によって変化する。このため、網点線数の判定を、通常速度より速い速度でのプレスキャンデータにて行う場合や、本スキャンデータで行う場合であっても、変倍時には、主走査方向と副走査方向とで倍率が変化する。
【０１７１】
例えば、図１６（ａ）に示されるような通常速度のスキャンでの画像データを、副走査方向に２倍速スキャン（すなわち倍率５０％）した場合のデータが図１６（ｂ）に、副走査方向に１／２倍速スキャン（すなわち倍率２００％）した場合のデータが図１６（ｃ）に示される。図１６（ａ）と、図１６（ｂ）または図１６（ｃ）とを比較すると、いずれの場合も主走査方向の反転回数は同じであるが、副走査方向での反転回数が異なる。この副走査方向の反転回数は、以下の（４）式により補正することができる。
補正後の反転回数＝補正前の反転回数×α×γ／β　・・・（４）
ただし、α＝倍率（％）／１００、β＝副走査方向のマスクサイズ、γ＝反転回数計数対象副走査方向サイズとする。なお、反転回数計数対象副走査方向サイズは、反転回数を計数した副走査方向のブロック数を示している。
【０１７２】
すなわち、図１６（ｂ）の場合、α＝０．５、β＝１０、γ＝１０となり、補正前の反転回数に０．５を乗算した値となる。また、上記（４）式は、斜め方向の反転回数の補正にも同様に適用可能である。例えば、図１３（ａ）の斜め約７２°で副走査方向１／２倍速スキャン（すなわち倍率２００％）の場合、α＝２、β＝１０、γ＝４となり、補正前の反転回数に０．８を乗算した値となる。
【０１７３】
このように、スキャン速度に応じて、主走査方向、副走査方向、および斜め方向の各方向での反転回数計数結果の補正を行うことで、副走査方向における誤差が解消され、より精度の良い結果が得られる。それゆえ、通常スキャンより速度の速いプレスキャンにて、網点線数の判定を行う構成の場合や、本スキャンにて判定を行う構成での変倍時に、精度よく網点線数を判定できる。
【０１７４】
なお、第１網点領域判定部１９ａ、第２網点領域判定部３４ともこの構成としてもよく、また、第１網点領域判定部および第２網点領域判定部を共通の構成としてもよい。
【０１７５】
また、ＡＤＦ（自動原稿画像読取装置）などで、原稿画像を読み取り、粗いデータを基にして前処理を行う場合、すなわち、読取データのサンプリング条件を変えた時も同様である。
【０１７６】
次に、網点線数判定部９のさらに別の構成について説明する。図１７に示されるように、網点線数判定部９ｃは、図７の網点線数判定部９ａの構成に加えて、フィルタ処理部３８を備えていてもよい。網点線数判定部９ｃは、入力データの２値化処理を行う第２網点領域判定部３４の前段にフィルタ処理部３８を備えている。
【０１７７】
フィルタ処理部３８は、入力信号の２値化処理前に、フィルタ処理にて画像強調を行う。フィルタ処理部３８におけるフィルタ係数は、例えば、ラプラシアンフィルタのような、一般的な微分フィルタ処理を行う係数でよい。
【０１７８】
このように、入力信号の２値化処理前に、フィルタ処理部３８にて画像強調を行うことで、網点の特徴である特定ピッチでの濃度差がはっきりする。その結果、反転回数がより精度よく計数できる。
【０１７９】
なお、第１網点領域判定部１９ａおよび第２網点領域判定部３４ともにフィルタ処理部３８を備える構成としてもよく、また、第１網点領域判定部および第２網点領域判定部を共通の構成としてもよい。
【０１８０】
〔実施の形態２〕
本発明の他の実施の形態について図１８ないし図２６に基づいて説明すれば、以下の通りである。なお、説明の便宜上、前記実施の形態１にて示した各部材と同一の機能を有する部材には、同一の符号を付記し、その説明を省略する。
【０１８１】
図１８に示されるように、本実施形態のデジタルカラー複写機（画像形成装置）４０は、前述の実施の形態１における第１網点判定部８の代わりに原稿種別自動判別部４１を適用するものである。実施の形態１では第１網点判定部８により原稿画像中の網点の有無を判定していたが、本実施形態では、原稿種別自動判別部４１により原稿画像の種別を判別する。
【０１８２】
原稿種別自動判別部４１は、原稿画像が、網点原稿または非網点原稿（例えば、文字原稿、印画紙写真原稿など）のいずれの原稿であるかを判別する。そして、この結果、原稿画像が網点原稿と判別された場合は、制御部１７に、網点線数判定部９のパラメータ（第２網点領域判定部のパラメータ設定部のパラメータ）を変更するような信号を出力する。
【０１８３】
すなわち、実施の形態１においては、原稿画像が網点領域を含むか否かを判定し、その判定結果に基づいて、制御部１７により網点線数判定部９のパラメータを変更していた。これに対して、本実施形態では、原稿画像が網点画像（印刷写真）、または、文字・網点画像（文字と網点画像が混在した画像）であるか否かを判定し、その判定結果に基づいて網点線数判定部９のパラメータを変更するものである。なお、本実施形態における原稿画像の読み取り動作については、後述する。
【０１８４】
次に、原稿種別自動判別部４１の構成について説明する。原稿種別自動判別部４１は、図２０に示されるように、図４に示される第１網点判定部８の構成と同様である。しかし、第１網点判定部８では、総画素数に対する網点領域画素の比と第２判定部２６に設定されたパラメータとを比較することにより、原稿画像が網点を含むか否かを判定していた。これに対して、原稿種別判定部４１では、総画素数に対する網点領域画素の比を用いて、原稿画像が網点原稿か否かを判定する。
【０１８５】
原稿種別自動判別部４１は、例えば、領域分離に使用されるアルゴリズムでもって、原稿種別を判別することができる。
【０１８６】
第１網点領域判定部１９ｂの構成は、実施の形態１の第１網点領域判定部１９ａの構成とほぼ同様である。また、網点領域画素数の判定方法もほぼ同様である。すなわち、図２１に示されるように、第１網点領域判定部１９ｂは、２値化処理部２７、反転回数計数部２８、反転回数総和算出部４２、反転回数判定部３０、およびパラメータ設定部３１ａを備えている。図５に示される第１網点領域判定部１９ａの構成と比較すると、図５の最大値選択部２９が図２１では反転回数総和算出部４２となっている以外は、同様である。
【０１８７】
反転回数総和算出部４２は、注目画素を含むマスク内画素に対して、主走査方向および副走査方向の反転回数を計数する。そして、計数した結果のうち、所定の反転回数以上を示すラインの総和を算出する。算出された総和は、反転回数判定部３０にて、パラメータ設定部３１に設定されたパラメータａと比較される。これにより、注目画素が、網点領域画素であるか、非網点領域画素であるかが判定される。
【０１８８】
第１判定部２０は、前述のように、文字領域判定部１８および第１網点領域判定部１９ｂの判定結果に基づいて、原稿画像の各画素がそれぞれどの領域（本実施形態では、網点領域、文字領域、その他の領域（例えば、印画紙写真など））の画素であるかを判定する。
【０１８９】
また、総合判定部２１は、前述したように、文字領域画素カウンタ、網点領域カウンタ、総画素数カウンタ、除算器、第５判定部を備えている。そして、除算器にて総画素数に対する、文字領域画素、網点領域画素、およびその他の領域の画素がそれぞれ算出される。そして、この結果を元に最終的に原稿画像の種類が判別される。
【０１９０】
原稿画像は、例えば、文字原稿、網点原稿、文字・網点原稿、文字原稿、その他の原稿に分類することができる。ここで、その他の原稿とは、第１判定部にて網点領域でも文字領域でもないと判定された場合の原稿を意味する。例えば、原稿画像のうち、下地や印画紙写真領域は、その他の領域の画素と判定される。このため、原稿画像では、その大半の画素がその他の領域画素と判定される場合が多い。したがって、例えば、網点領域画素、または文字領域画素が全画素数の１％以上ある場合は、網点原稿、または文字原稿として、原稿画像を分類できる。このような、原稿種別の分類方法の一例を表２に示す。
【０１９１】
【表２】

【０１９２】
表２では、原稿画像を４種類（文字原稿、網点原稿、文字・網点原稿、その他の原稿）に分類している。しかし、原稿種別自動判別部４１では、網点原稿か非網点原稿かを分類でいればよい。したがって、網点領域が１％以上存在する原稿（すなわち、網点原稿および文字・網点原稿）を網点原稿と判別し、網点領域が１％未満の原稿を非網点原稿と判別できるように設定すればよい。
【０１９３】
このようにして、原稿種別自動判別部４１にて原稿画像が網点原稿であると判定された場合、後段の網点線数判定部９にて網点線数の検出処理が行われる。
【０１９４】
次に、本実施形態における網点線数判定部９ｄについて説明する。図２２に示されるように、網点線数判定部９ｄは、前述した図７に示される網点線数判定部と略同様の構成である。ただし、網点線数判定部９ｄは、第２網点領域判定部３４に反転回数総和算出部４２を備えている点が異なっている。
【０１９５】
デジタルカラー複写機４０における、原稿種別自動判別部４１、網点線数判定部９、および制御部１７の画像処理は、例えば、図１９に示されるフローチャートのように行われる。図１９に示される画像処理は、前述の実施の形態１にて説明した図３に示される画像処理と略同様である。すなわち、まず、本スキャンに先立って第１スキャン（プレスキャン）が行われる（Ｓ３７）。次に、原稿種別自動判定部４１にて、得られた入力信号データから、原稿画像が網点原稿であるか否か判定される（Ｓ３８、Ｓ３９）。このとき、網点原稿でないと判定されれば、網点線数判定部９にて網点線数判定は行わず（Ｓ４０）処理を終了して、本スキャン処理に入る。一方、網点原稿であると判定されれば、制御部１７により、網点線数判定部９のパラメータが変更される（Ｓ４１）。次に、網点線数の判定を行う第２スキャンが実行され、変更されたパラメータを用いて、網点線数判定部９では網点線数が判定され（Ｓ４２）、本スキャン処理に入る。本スキャン以降の処理は、網点線数判定結果に応じて、後段の領域分離パラメータ・フィルタ係数・ディザパターンなどが選択され、最適な処理が行われる。
【０１９６】
このように、原稿画像の種別を判別した後、網点線数に応じた各処理がなされるため、鮮鋭度を保ちながらモアレのない出力画像を得ることが可能となる。なお、本スキャンは、上記Ｓ３７〜Ｓ４２までに得られた原稿画像の情報をもとに最適な画像処理を行うためのスキャンである。
【０１９７】
また、第１スキャンでは、文字領域、網点領域、その他の領域の各画素数が算出される。第１スキャンでは、原稿種別の判別が目的であるため、原稿種別判別部４１に設定されるパラメータは、いかなる網点であっても検出可能な設定となっている。
【０１９８】
これに対して、第２スキャンでは、網点線数の検出が目的であるため、網点線数判定部に設定されるパラメータは、制御部により高網点線数のみを検出可能となるように変更される。例えば、網点原稿と判定された場合、原稿種別自動判別部のパラメータよりも網点線数判定部のパラメータが大きくなるように変更される。
【０１９９】
ここで、具体例を挙げて、原稿種別自動判別部４１による原稿画像の判別方法と、網点線数判定部９の判定方法について詳細に説明する。ここでは、原稿種別自動判別部４１での閾値を、注目画素を含む７×７のマスク内の平均濃度として原稿画像の２値化を行った結果、白→黒あるいは黒→白へと画素が反転する回数を計数することにする。
【０２００】
図２３（ａ）〜図２３（ｃ）は、６５線（図２３（ａ））、１００線（図２３（ｂ））、２００線（図２３（ｃ））といった、低線数から高線数の網点の平均濃度を閾値として、２値化した結果を示したものである。また、欄外には、これらの結果のそれぞれについて、反転回数を計数した結果が示される。
【０２０１】
これら３つの結果をみると、６５線（図２３（ａ））、１００線（図２３（ｂ））、２００線（図２１（ｃ））の順に、線数の増加に伴い、反転回数が増加していることがわかる。
【０２０２】
また、図２４は、さらに、いろいろな線数の網点原稿を、図２３のように７×７のマスクサイズで反転回数を計数し、その反転回数が２回以上である全画素を検出した例を示している。この結果、所定回数以上（ここでは２回以上）の反転回数を有する数と、網点線数とが相関しているのが分かる。すなわち、図２３の場合と同様に、線数の増加に伴い、反転回数が増加している。
【０２０３】
したがって、この性質を利用して、第２スキャン時の網点検出時における反転回数の設定値（パラメータ設定部の設定値）を第１のスキャン時の設定値よりも大きくすれば、第２スキャンでは低線数の網点は検出されず、高線数の網点のみが検出されることになる。
【０２０４】
例えば、第１スキャンでは原稿種別を目的とするため、反転回数の設定値を“１”に設定する。これにより、第１スキャンでは、１回以上の反転回数が全て検出される。すなわち、低線数の網点もすべて検出される。一方、第２スキャンでは、所定の網点線数以上を検出することが目的であるため、反転回数の設定値を“２”に設定する。これにより、第１スキャンにおいて網点として検出された反転回数が“１”の部分（マスク画素）は、第２スキャンでは、網点として検出されなくなる。
【０２０５】
したがって、第１スキャンで網点と判定された画素数に対する、第２スキャンで網点と判定された画素数の比率（網点画素数比）が１００％に近い場合は、高線数の網点と判断することができる。逆に、当該比率が０％に近い場合は、低線数の網点と判断することができる。
【０２０６】
図２５には、種々の線数の網点原稿における上記網点画素数の比を縦軸に、網点線数を横軸にしたグラフが示される。図２５から明らかなように、網点線数と上記網点画素数比とが、相関している。そして、網点線数は、おおまかに低線数、中線数、高線数の３種類程度に分類することができる。また、図示しないが、中線数は、さらに、実施の形態１のように中線数を高線数よりの中線数と低線数よりの中線数に分類し、全体として４段階に分類することもできる。
【０２０７】
このように網点の線数を多段階に分けることができると、後段の領域分離パラメータ・フィルタ処理の係数や、中間調処理のディザパターンを最適な設定にすることが可能となる。したがって、領域分離パラメータ・フィルタ処理の係数、中間調処理のディザパターンを最適化した後、最終本スキャン（全画像処理動作）である第３スキャンを行うことにより網点線数に対し、最適な画像処理を施すことが可能となる。これにより、網点線数に応じた各処理がなされるため、鮮鋭度を保ちながらモアレのない出力画像を得ることが可能となる。
【０２０８】
なお、上記の説明では文字領域判定部と網点領域判定部を設け、文字領域でも網点領域でもない場合をその他領域と判別したが、注目画素を含むｎ×ｍのブロックの最大濃度差や総和濃度繁雑度を求め、これらの特徴量を基に写真領域であるか否かを判定する写真領域判定部を文字領域判定部・網点領域判定部と並列に設けるような構成としても良い（実施の形態１参照）。
【０２０９】
上記の説明では、原稿種別自動判別部４１、網点線数判別部９、領域分離処理部を個別に設ける構成について説明した。次に、各処理部にて共通の処理を行い、各処理部を一つの回路で構成しＣＰＵにより処理を切り換えるようにする構成について説明する。
【０２１０】
図２６は、原稿種別自動判別部４１、網点線数判定部９、領域分離処理部１１の各処理部が一体型となった構成の例を示したものである（図１８参照）。この場合、各処理部は、以下の構成からなる。
【０２１１】
原稿種別自動判別部４１は、前述のように、文字領域判定部１８、第１網点領域判定部１９ｃ、第１判定部２０、文字領域画素カウンタ２２、網点領域画素カウンタ２３ｇ、総画素数カウンタ２４、除算器２５、第２判定部２６から構成される。
【０２１２】
網点線数判定部９は、文字領域判定部１８、第１網点領域判定部１９ｃ、第１判定部２０、網点領域画素カウンタ２３ｇ、網点線数判定部３５、閾値設定部３６から構成される。
【０２１３】
領域分離処理部１１は、文字領域判定部１８、第１網点領域判定部１９ｃ、第１判定部２０から構成され、例えば、前記表２の判定基準により、原稿画像の領域識別信号が出力される。
【０２１４】
原稿種別自動判別部４１は、第２判定部２６から原稿種別判別信号を出力する。
【０２１５】
網点線数判定部９は、原稿種別自動判別部４１の結果より網点原稿が含まれると判断された場合、制御部１７にて、網点と判定する際のパラメータ設定部の値が変更される。網点線数判別部３５からは、網点線数判別結果が出力される。
【０２１６】
本発明において、原稿種別の判別方法としては、本実施形態で示したような領域分離処理のアルゴリズムに限定されるものではなく、一般的な方法であるヒストグラムを作成して判別するものであっても良い。本発明を実現する上では、これらの原稿種別の判別方法と共に、網点画素の特徴量（例えば、総和濃度繁雑度やランレングス）を抽出し、この特徴量を用いて網点領域の画素であるか否かを判定する際の判定基準を、原稿種別を判別する時と網点線数の判別を行う時とで変更可能であるならばどのような方法であっても良い。
【０２１７】
また、第１スキャンで網点画像ではないと判断された場合は、第２スキャンすなわち網点線数検出処理をスキップさせ第３スキャンを行うことにより第２スキャンで要してした時間を短縮することが可能となる。さらに、時間を短縮する方法としては、以下に示すスキャン速度を上げる方法が挙げられる。
【０２１８】
ここでは、６００ｄｐｉ（ｄｏｔ　ｐｅｒ　ｉｎｃｈ）の解像度を１００ｍｍ／ｓ（第３スキャンの速度）で読み取りを行うスキャナを例に挙げて説明する。この場合、本スキャンである第３スキャン以外、すなわち第１、第２スキャンの速度は２００ｍｍ／ｓとする。また、ＣＣＤからの画像信号タイミングは変更せず、スキャン速度だけ倍速にするため、副走査の解像度は、通常解像度の半分（すなわち３００ｄｐｉ）となる。
【０２１９】
第１スキャンおよび第２スキャンは、原稿種別判別および網点線数の検出のためだけに行うため、通常解像度の半分でほぼ同等の原稿種別判別及び網点線数の検出結果を得ることが可能である。従って、原稿種別判別及び網点線数検出の精度は落さず、スキャン速度を上昇させた分だけ、時間を短縮できる。
【０２２０】
さらに、原稿種別判別、網点線数検出に要する時間を短縮する別の方法としては、第２スキャンを第１スキャンのリターン（復路）時に行う方法が挙げられる。この場合、第１スキャンにより検出する画像と、第２スキャンにより検出する画像とは、同サイズ及び同解像度（同スキャン速度）とすることが好ましい。すなわち、原稿画像の同じ範囲の画像データを用いて網点線数が判定されることが好ましい。これにより、精度よく原稿画像の網点線数が判定することができる。
【０２２１】
このため、第１スキャンのリターン時に第２スキャンを行う場合は、図２８に示されるように、フィード（往路）からリターン（復路）に切り換え、第１スキャンと同じスキャンスピードを得るために、原稿読み取り範囲に加えて、助走部分を設けることが好ましい。その理由は、以下に示す通りである。すなわち、スキャナを駆動しているモーターは、等速回転になるまで一定時間を要する。従って、等速回転になるまでの時間は、画像読取り速度が変化することになる。画像読取り速度が変化している状態で原稿画像の読取りを行うと、所望の画像データを読取ることが困難である。従って、等速回転になるまでの時間を助走部分とすることで、適正な画像読取りが可能となる。これにより、精度よく原稿画像の網点線数を判定することができる。なお、助走部分は、網点画像検出に使用しないように制御し第２スキャンを行う。
【０２２２】
また、第１、第２、第３のスキャン長及びスキャン画像範囲を原稿サイズに応じて切り換えるよう構成とすることにより、Ａ３原稿よりも通常よく使用されるＡ４原稿のほうが速度アップにつながる。原稿サイズを検知する方法としては、例えば一般的に公知である、原稿台内部に原稿サイズに応じて複数の光電センサを設け光検知の有無により検出する方法を用いれば良い。第３スキャンは原稿画像のサイズ分のスキャンは必要となるが、第１及び第２スキャンの画像サイズは略同等として原稿サイズよりも小さくスキャンしてもよい。
【０２２３】
以上のように、実施の形態１および実施の形態２では、デジタルカラー複写機（画像形成装置）について説明したが、本発明にかかる画像処理装置は、図２７に示されるような、フラットベッド・スキャナ５０（画像読取装置）などとして実現することも可能である。図２７に示したフラットベッド・スキャナ５０（画像読取装置）は、図１または図１８のデジタルカラー複写機１および４０（画像形成装置）と比較すると、領域分離機能を保持しない（すなわち領域識別信号が出力されない）点が異なるのみであり、装置構成は略同様である。
【０２２４】
フラットベッド・スキャナ５０（画像読取装置）により読み取られた画像データ・原稿種別の判別結果、および網点線数の判別結果は、ネットワークを介して画像処理サーバーやプリンタに、あるいは、ＳＣＳＩ（Ｓｍａｌｌ　Ｃｏｍｐｕｔｅｒ　Ｓｙｓｔｅｍ　Ｉｎｔｅｒｆａｃｅ）やＵＳＢ（Ｕｎｉｖｅｒｓｅ　Ｓｅｒｉａｌ　Ｂｕｓ）などのインターフェースを介してコンピュータに入力される。尚、画像読取装置の画像処理装置としては、図１または図１８に記載されている入力階調補正処理部１０や領域分離処理部１１などを備えるようにしても良い。
【０２２５】
なお、本発明は、コンピュータに実行させるためのプログラムを記録したコンピュータ読み取り可能な記録媒体に、画像処理方法を記録するものとすることもできる。
【０２２６】
この結果、画像処理方法を行うプログラムを記録した記録媒体を持ち運び自在に提供することができる。
【０２２７】
なお、本発明においては、この記録媒体としては、マイクロコンピュータで処理が行われるために図示していないメモリ、例えばＲＯＭのようなものそのものがプログラムメディアであってもよいし、また、図示していないが外部記憶装置としてプログラム読み取り装置が設けられ、そこに記録媒体を挿入することで読み取り可能なプログラムメディアであってもよい。
【０２２８】
いずれの場合においても、格納されているプログラムはマイクロプロセッサがアクセスして実行させる構成であってもよいし、あるいは、いずれの場合もプログラムを読み出し、読み出されたプログラムは、マイクロコンピュータの図示されていないプログラム記憶エリアにダウンロードされて、そのプログラムが実行される方式であってもよい。このダウンロード用のプログラムは予め本体装置に格納されているものとする。
【０２２９】
ここで、上記プログラムメディアは、本体と分離可能に構成される記録媒体であり、磁気テープやカセットテープ等のテープ系、フロッピーディスクやハードディスク等の磁気ディスクやＣＤ−ＲＯＭ／ＭＯ／ＭＤ／ＤＶＤ等の光ディスクのディスク系、ＩＣカード（メモリカードを含む）／光カード等のカード系、あるいはマスクＲＯＭ、ＥＰＲＯＭ（Ｅｒａｓａｂｌｅ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＥＥＰＲＯＭ（ＥｌｅｃｔｒｉｃａｌｌｙＥｒａｓａｂｌｅ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、フラッシュＲＯＭ等による半導体メモリを含めた固定的にプログラムを担持する媒体であっても良い。
【０２３０】
また、本実施の形態においては、インターネットを含む通信ネットワークを接続可能なシステム構成であることから、通信ネットワークからプログラムをダウンロードするように流動的にプログラムを担持する媒体であっても良い。なお、このように通信ネットワークからプログラムをダウンロードする場合には、そのダウンロード用のプログラムは予め本体装置に格納しておくか、あるいは別な記録媒体からインストールされるものであっても良い。
【０２３１】
上記記録媒体は、デジタルカラー複写機やコンピュータシステムに備えられるプログラム読み取り装置により読み取られることで上述した画像処理方法が実行される。
【０２３２】
コンピュータシステムは、フラットベッドスキャナ・フィルムスキャナ・デジタルカメラなどの画像入力装置、所定のプログラムがロードされることにより上記画像処理方法など様々な処理が行われるコンピュータ、コンピュータの処理結果を表示するＣＲＴディスプレイ・液晶ディスプレイなどの画像表示装置およびコンピュータの処理結果を紙などに出力するプリンタより構成される。さらには、ネットワークを介してサーバーなどに接続するための通信手段としてのモデムなどが備えられる。
【０２３３】
本発明は上述した各実施形態に限定されるものではなく、請求項に示した範囲で種々の変更が可能であり、異なる実施形態にそれぞれ開示された技術的手段を適宜組み合わせて得られる実施形態についても本発明の技術的範囲に含まれる。
【０２３４】
【発明の効果】
本発明にかかる画像処理装置は、以上のように、入力画像データから原稿画像の網点線数を判定することにより、出力画像を得るための出力画像データを生成する画像処理装置において、原稿画像の網点領域画素数の算出結果に基づき、原稿画像の網点線数を識別する網点線数識別手段を備えていることを特徴としている。
【０２３５】
したがって、簡単な構成にて原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２３６】
本発明にかかる画像処理装置は、以上のように、上記網点線数識別手段は、原稿画像の網点領域画素数の算出結果に基づき、原稿画像が網点を含むか否かを判定する第１網点判定手段と、原稿画像の網点領域画素数の算出結果に基づき、原稿画像の網点線数を判定する網点線数判定手段と、上記第１網点判定手段の判定結果に応じて、上記網点線数判定手段を制御する制御手段とを備えていることを特徴としている。
【０２３７】
したがって、原稿画像が網点を含むか否かを判定することにより原稿画像の網点線数判定を行うか否か決定し、網点領域画素の算出結果に基づいて、原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２３８】
本発明にかかる画像処理装置は、以上のように、上記網点線数識別手段は、原稿画像の網点領域画素数の算出結果に基づき、原稿画像が網点原稿であるか否かを判定する原稿種別自動判別手段と、原稿画像の網点領域画素数の算出結果に基づき、原稿画像の網点線数を判定する網点線数判定手段と、上記原稿種別自動判別手段の判定結果に応じて、上記網点線数判定手段を制御する制御手段とを備えることを特徴としている。
【０２３９】
したがって、原稿画像が網点原稿であるか否かを判定することにより原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２４０】
本発明にかかる画像処理装置は、以上のように、上記網点線数判定手段は、原稿画像の各画素の濃度変化を計数することにより、網点領域画素を判定する網点領域判定手段と、原稿画像の網点線数を判別するための閾値を格納する閾値設定手段と、上記第１網点判定手段または原稿種別自動判別手段により算出される網点領域画素数に対する上記網点線数判定手段により算出される網点領域画素数の比を、上記閾値と比較することにより、網点線数を判別する網点線数判別手段とを備えることを特徴としている。
【０２４１】
したがって、原稿画像の濃度変化（例えば、反転回数）を計数することにより、原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２４２】
本発明にかかる画像処理装置は、以上のように、上記閾値設定手段は、複数の閾値を格納していることを特徴としている。
【０２４３】
したがって、網点線数の判定は、多段階に行われる。その結果、網点線数を細かく判別できるという効果を奏する。これにより、一層、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２４４】
本発明にかかる画像処理装置は、以上のように、上記第１網点判定手段、上記原稿種別自動判別手段、および上記網点線数判定手段は、入力画像データを２値化データとする２値化処理手段と、上記２値化データにおける２値の切り替わり回数である反転回数を計数する反転回数計数手段とを備え、上記反転回数の最大値または総和により網点領域画素数を算出することを特徴としている。
【０２４５】
したがって、原稿画像を２値化したデータの反転回数の最大値または総和を計数することにより、原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２４６】
本発明にかかる画像処理装置は、以上のように、上記の構成において、さらに、上記２値化処理手段による処理の前に、入力画像データのフィルタ処理を行うフィルタ処理手段を備えていることを特徴としている。
【０２４７】
したがって、入力画像データは、網点の特徴である特定ピッチでの濃度差が明確になる。これにより、より精度よく反転回数が計数できるという効果を奏する。
【０２４８】
本発明にかかる画像処理装置は、以上のように、上記反転回数計数手段は、原稿画像を構成する１つの画素である注目画素を含む複数の画素からなるブロックに対して、主走査方向、当該主走査方向に対して略垂直方向である副走査方向、および当該副走査方向とは異なる当該主走査方向に対して任意の角度の方向に、反転回数の計数を行うことを特徴としている。
【０２４９】
したがって、原稿画像がスクリーン角度を有する網点の場合、または原稿画像の設置方向に関わらず、反転回数を計数できるという効果を奏する。それゆえ、精度よく網点線数を判定することができるという効果を奏する。
【０２５０】
本発明にかかる画像処理装置は、以上のように、上記反転回数計数手段は、原稿画像を読み込む条件に応じて反転回数の計数結果を補正する補正手段を備えることを特徴としている。
【０２５１】
したがって、スキャン速度に応じて各走査方向における反転回数の補正を行えば、反転回数の誤差が解消される。その結果、精度よく反転回数を計数できるという効果を奏する。これにより、特に、通常スキャンより速い速度のプレスキャンにより反転回数を計数する場合、および、本スキャンにて反転回数を計数する場合、の反転回数の誤差を解消できるという効果を奏する。
【０２５２】
本発明にかかる画像処理装置は、以上のように、上記補正手段は、上記反転回数計数手段により反転回数を計数する全ての方向において、反転回数を計数する対象となる画素数を等しくすることを特徴としている。
【０２５３】
したがって、全ての方向に計数された反転回数は、同一条件で計数される。その結果、常に、原稿画像中の反転回数の最大値を選択することが可能である。これにより、精度よく原稿画像の網点線数が判定することができるという効果を奏する。
【０２５４】
本発明にかかる画像処理装置は、以上のように、上記網点判定手段または上記原稿種別自動判定手段による処理は、画像入力手段により原稿画像を読み込む際の往路時に行い、上記網点線数判定手段による処理は、画像入力手段により原稿画像を読み込む際の復路時に行うことを特徴としている。
【０２５５】
したがって、本来２回スキャンを行うべきところを１回のスキャンとすることができるという効果を奏する。その結果、原稿画像の網点および網点線数の判定に要する時間が短縮できるという効果を奏する。これにより、画像形成装置に応用すれば、コピースピードも短縮できるという効果を奏する。
【０２５６】
本発明にかかる画像処理装置は、以上のように、上記網点線数判定手段は、原稿画像における所定の範囲内の画像データを用いて網点線数を判定することを特徴としている。
したがって、原稿画像の特徴を表す部分および画像データの信頼性の高い部分の網点線数を判定できるという効果を奏する。その結果、精度よく原稿画像の網点線数を判定することができるという効果を奏する。
【０２５７】
本発明にかかる画像処理装置は、以上のように、上記網点判定手段または上記原稿種別自動判別手段により処理を行うために原稿画像を読み取る範囲の画像データと、上記網点線数判定手段により処理を行うために原稿画像を読み取る範囲の画像データとが、略同一であることを特徴としている。
【０２５８】
したがって、原稿画像の同じ範囲の画像データを用いて網点線数が判定される。これにより、精度よく原稿画像の網点線数が判定することができるという効果を奏する。
【０２５９】
本発明にかかる画像処理装置は、以上のように、上記制御手段は、上記第１網点判定手段により原稿画像が網点を含む、または上記原稿種別判別手段により原稿画像が網点原稿であると判定された場合、上記第１網点判定手段または原稿種別自動判別手段により網点領域画素と判定するための第１の閾値よりも、上記網点線数判定手段により網点領域画素と判定するための第２の閾値を大きくするように制御することを特徴としている。
【０２６０】
したがって、例えば、反転回数が少ない画素（すなわち、低線数の網点）は、第１網点判定手段では網点領域画素と判定されても、網点線数判定手段では網点領域画素と判定されなくなる。これにより、原稿画像の網点線数が判定することができるという効果を奏する。
【０２６１】
本発明にかかる画像処理装置は、以上のように、上記制御手段は、上記網点判定手段により原稿画像に網点が含まれないと判定された場合、または、上記原稿種別自動判別手段により原稿画像が非網点原稿と判定された場合に、上記網点線数判定手段による処理を行わないように制御することを特徴としている。
【０２６２】
したがって、原稿画像の網点および網点線数の判定に要する時間が短縮できるという効果を奏する。これにより、画像形成装置に応用すれば、コピースピードも短縮できるという効果を奏する。
【０２６３】
本発明にかかる画像読取装置は、以上のように、上記したいずれかの画像処理装置を備えることをとしている。
【０２６４】
したがって、簡単な構成にて原稿画像中の網点の特徴量を抽出し、網点線数を判定できる画像読取装置を提供できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２６５】
本発明にかかる画像形成装置は、以上のように、上記したいずれかの画像処理装置を備えることを特徴としている。
【０２６６】
したがって、簡単な構成にて原稿画像中の網点の特徴量を抽出し、網点線数を判定できる画像形成装置を提供できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２６７】
本発明にかかる画像処理方法は、以上のように、入力画像データから原稿画像の網点線数を判定することにより、出力画像を得るための出力画像データを生成する画像処理方法において、原稿画像の網点領域画素の算出結果に基づき、原稿画像が網点を含むか否かを判定する網点判定処理と原稿画像の網点領域画素数の算出結果に基づき、原稿画像の網点線数を判定する網点線数判定処理と、上記網点判定処理の結果に応じて、上記網点線数判定処理を制御する制御処理とを含むことを特徴としている。
【０２６８】
したがって、原稿画像が網点を含むか否かを判定することにより原稿画像の網点線数判定を行うか否か決定し、網点領域画素の算出結果に基づいて、原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２６９】
また、本発明にかかる画像処理方法は、以上のように、入力画像データから原稿画像の網点線数を判定することにより、出力画像を得るための出力画像データを生成する画像処理方法において、原稿画像の網点領域画素数の算出結果に基づき、原稿画像が網点原稿であるか否かを判定する原稿種別自動判別処理と、原稿画像の網点領域画素数の算出結果に基づき、原稿画像の網点線数を判定する網点線数判定処理と、上記原稿種別自動判別処理の判定結果に応じて、上記網点線数判定処理を制御する制御処理とを含むことを特徴としている。
【０２７０】
したがって、原稿画像が網点原稿であるか否かを判定することにより原稿画像の網点線数判定を行うか否か決定し、網点領域画素の算出結果に基づいて、原稿画像中の網点の特徴量を抽出し、網点線数を判定できるという効果を奏する。これにより、判定された網点線数に適した画像処理を行うことができるので、出力画像の画質が向上するという効果を奏する。
【０２７１】
本発明にかかるプログラムは、以上のように、上記したいずれかの画像処理方法をコンピュータに実行させることを特徴としている。
【０２７２】
したがって、簡単な構成にて原稿画像中の網点の特徴量を抽出し、網点線数を判定できる画像処理方法をコンピュータが読み取り実行することができるプログラムを提供できるという効果を奏する。それゆえ、本発明にかかる画像処理方法を汎用的なものとすることができるという効果を奏する。
【０２７３】
本発明の記録媒体は、以上のように、上記プログラムをコンピュータに読み取り可能に格納していることを特徴としている。
【０２７４】
したがって、簡単な構成にて原稿画像中の網点の特徴量を抽出し、網点線数を判定できる画像処理方法のプログラムを容易にコンピュータに供給できる記録媒体を提供できるという効果を奏する。
【図面の簡単な説明】
【図１】本発明の実施の形態１におけるカラー画像形成装置の構成を示すブロック図である。
【図２】本発明の実施の形態１におけるタンデム方式を適用したカラー画像形成装置の概略構成を示す断面図である。
【図３】図１の第１網点判定部、網点線数判定部、および制御部における画像処理の流れを示すフローチャートである。
【図４】図１の第１網点判定部の構成を示すブロック図である。
【図５】図４の網点領域判定部の構成を示すブロック図である。
【図６】図５の網点領域判定部における処理の流れを示すフローチャートである。
【図７】図１の網点線数判定部の構成を示すブロック図である。
【図８】図１の網点線数判定部の別の構成を示すブロック図である。
【図９】図７または図８における網点線数の判定の流れを示すフローチャートである。
【図１０】図７または図８における別の網点線数の判定の流れを示すフローチャートである。
【図１１】図７または図８の反転回数計数部の構成を示すブロック図である。
【図１２】図１１の反転回数計数部における反転回数の計数方法を示す図であり、図１２（ａ）は主走査方向に反転回数を計数する例であり、図１２（ｂ）は副走査方向に反転回数を計数する例であり、図１２（ｃ）は斜め＋４５°方向に反転回数を計数する例であり、図１２（ｄ）は斜め−４５°方向に反転回数を計数する例である。
【図１３】図１１の反転回数計数部における反転回数の計数方法を示す図であり、図１３（ａ）は斜め＋７２°方向に反転回数を計数する例であり、図１３（ｂ）斜め−７２°方向に反転回数を計数する例であり、図１３（ｃ）斜め約−２２°方向に反転回数を計数する例であり、図１３（ｄ）は斜め約＋６３°方向に反転回数を計数する例である。
【図１４】図１２（ａ）〜図１３（ｂ）の反転回数計数方法により、網点パターンの反転回数を計数した結果を示す図である。
【図１５】図１２（ａ）〜図１３（ｄ）の反転回数計数方法により、網点パターンの反転回数を計数した結果を示す図である。
【図１６】同一の画像データについてスキャン速度を変化させた場合における反転回数の計数結果を示す図であり、図１６（ａ）は通常のスキャン速度の場合における反転回数の計数結果であり、図１６（ｂ）は副走査方向に通常の２倍のスキャン速度の場合における反転回数の計数結果を示す図であり、図１６（ｃ）は副走査方向に通常の０．５倍のスキャン速度の場合における反転回数の計数結果を示す図である。
【図１７】図１の網点線数判定部の別の構成を示すブロック図である。
【図１８】本発明の実施の形態２におけるカラー画像形成装置の構成を示すブロック図である。
【図１９】図１８の原稿種別自動判別部、網点線数判定部、および制御部における画像処理の流れを示すフローチャートである。
【図２０】図１８の原稿種別自動判別部の構成を示すブロック図である。
【図２１】図２０の第１網点領域判定部の構成を示すブロック図である。
【図２２】図１８の網点線数判定部の構成を示すブロック図である。
【図２３】種々の網点線数の画像データを平均濃度を閾値として２値化した結果を示す図であり、図２３（ａ）は６５線の網点を２値化した結果であり、図２３（ｂ）は１００線の網点を２値化した結果であり、図２３（ｃ）は２００線の網点を２値化した結果である。
【図２４】網点線数と反転回数との関係を示したグラフである。
【図２５】網点線数と網点領域画素比との関係を示したグラフである。
【図２６】図１６の原稿自動種別判別部、網点線数判別部、領域分離処理部を１つの回路により構成したブロック図である。
【図２７】本発明の実施の一形態にかかる画像読取装置の構成を示すブロック図である。
【図２８】本発明の実施の一形態にかかる画像処理に要する時間を短縮する方法を説明する図である。
【符号の説明】
１　　デジタルカラー複写機（画像形成装置）
８　　第１網点判定部（第１網点判定手段、網点線数識別手段）
９　　網点線数判定部（網点線数判定手段、網点線数識別手段）
１７　制御部（制御手段、網点線数識別手段）
１９　第１網点領域判定部（網点領域判定手段）
２７　２値化処理部（２値化処理手段）
２８　反転回数計数部（反転回数系数手段）
３５　網点線数判別部（網点線数判別手段）
３６　閾値設定部（閾値設定手段）
３８　フィルタ処理部（フィルタ処理手段）
４０　デジタルカラー複写機（画像形成装置）
４１　原稿種別自動判別部（原稿種別自動判別手段、網点線数識別手段）
５０　フラットベッド・スキャナ（画像読取装置）[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an image processing apparatus and an image processing method that determine the level of the number of halftone dots from an input image signal and perform appropriate processing based on the result, and an image reading apparatus, an image forming apparatus, and a program including the same. , A recording medium.
[0002]
[Prior art]
2. Description of the Related Art In a digital color image input device such as a digital scanner or a digital still camera, input color image data (color information) generally includes color information (R) of tristimulus values obtained by a color separation type solid-state imaging device (CCD). , G, B) are converted from analog signals to digital signals and used as input signals. When the signal input by the image input device is optimally displayed or output, separation is performed for each small area having the same characteristics in the read original image. By performing the optimal image processing on the area having the same characteristics, it is possible to reproduce a high-quality image.
[0003]
Generally, when a document image is separated into small regions, a process of identifying each region of a character region, a halftone dot region, and a photograph region (other regions) existing in the read document image is performed on a local basis. . The reproducibility of the image can be enhanced by switching the image quality improvement processing for each of the identified areas having the respective characteristics. Note that the original image can be applied to all images that can be handled by the image input device, such as landscapes and artificially formed images.
[0004]
By the way, in the case of the halftone dot area (image), 65 lines / mm, 85 lines / mm, 100 lines / mm, 120 lines / mm, 133 lines / mm, and 175 lines / mm, from the low line number to the high line number, Halftone dots are used. Therefore, a method has been proposed in which the halftone frequency is determined and appropriate processing is performed according to the result.
[0005]
For example, Japanese Patent Application Laid-Open No. Hei 1-133470 discloses that the correlation between read image information and reference data obtained from a halftone plate is determined, the presence or absence of a halftone dot is identified based on the magnitude of the correlation, and further, the halftone dot is sequentially determined. A method is described in which the halftone dot density (period) of the plate is switched, the magnitude of the correlation obtained at each density is compared, and the density with the highest correlation is determined as the halftone period. That is, in the discrimination method of this publication, the number of halftone dots on a document image is discriminated by pattern matching processing.
[0006]
Also, Japanese Patent Application Laid-Open No. 7-220072 discloses that the input image is subjected to one-dimensional Fourier transform for each line, and the one-dimensional Fourier transform output signal is classified for each spatial frequency, thereby obtaining a one-dimensional direction of the input image. A method of determining the dot period by calculating and detecting the spatial frequency characteristics of the above-described method is described. That is, the discrimination method disclosed in the publication uses a one-dimensional Fourier transform to discriminate the dot period.
[0007]
[Problems to be solved by the invention]
As described above, Fourier transform, which is pattern matching or frequency analysis, has been used for halftone detection or halftone frequency detection. However, in the image identification method using pattern matching, it is necessary to prepare many patterns. For this reason, there arise problems such as an enormous memory capacity and poor versatility. Further, the image identification method using the Fourier transform has the problems that the calculation is complicated even if it is performed by software and hardware, it is difficult to increase the speed, and it is not possible to configure an inexpensive system.
[0008]
The present invention has been made in view of the above problems, and an object of the present invention is to extract a feature amount of a halftone dot with a simple configuration without performing complicated and enormous processing of a circuit scale such as frequency analysis. Provided are an image processing apparatus, an image processing method, an image reading apparatus having the image processing apparatus, an image forming apparatus, an image processing program, and a computer-readable recording medium storing the image processing apparatus, the image processing method, and the image processing apparatus. Is to do.
[0009]
[Means for Solving the Problems]
An image processing apparatus according to the present invention is directed to an image processing apparatus that generates output image data for obtaining an output image by determining a halftone frequency of a document image from input image data in order to solve the above problem. The image processing apparatus further includes a halftone frequency determining unit that identifies the halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image.
[0010]
According to the above invention, the halftone frequency of the original image is determined based on the calculation result of the halftone area pixel number of the original image. That is, it is not necessary to determine the halftone frequency by performing a complicated and enormous process such as pattern matching and frequency analysis as in the related art.
[0011]
Therefore, the feature value of the halftone dot in the document image can be extracted with a simple configuration, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image. That is, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image without moire while maintaining sharpness.
[0012]
In order to solve the above problem, the image processing apparatus according to the present invention is configured such that the halftone frequency determining unit determines whether or not the original image contains halftone dots based on a calculation result of the number of halftone dot pixels of the original image. First halftone dot determination means for determining the halftone frequency of the original image, and halftone frequency determination means for determining the halftone frequency of the original image based on the calculation result of the number of halftone pixels in the original image; Control means for controlling the halftone frequency determining means according to the result.
[0013]
According to the invention described above, by calculating the halftone area pixels of the original image, it is determined whether or not the original image includes halftone dots, and when the original image includes halftone dots, based on the calculation result of the halftone area pixels. Thus, the halftone frequency of the document image is determined. That is, it is not necessary to determine the halftone frequency by performing a complicated and enormous processing such as a circuit scale such as pattern matching and frequency analysis as in the related art.
[0014]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image contains halftone dots. Based on the calculation result of the halftone area pixels, the characteristic of the halftone dots in the original image is determined. By extracting the quantity, the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0015]
In order to solve the above problem, the image processing apparatus according to the present invention, wherein the halftone frequency determining unit determines whether or not the original image is a halftone original based on the calculation result of the halftone area pixel number of the original image. A document type automatic discriminating means for judging whether or not, a halftone frequency determining means for judging the halftone frequency of the document image based on the calculation result of the halftone area pixel number of the document image, Control means for controlling the halftone frequency determining means in accordance with
[0016]
According to the above invention, by calculating the halftone area pixels of the original image, it is determined whether or not the original image is a halftone original, and the halftone frequency of the original image is determined. That is, it is not necessary to determine the halftone frequency by performing a complicated and enormous processing such as a circuit scale such as pattern matching and frequency analysis as in the related art. Note that the halftone original indicates, for example, a printed photograph or the like, and may include an original in which characters and halftone areas are mixed.
[0017]
Therefore, by determining whether or not the original image is a halftone original, the feature amount of the halftone dots in the original image can be extracted, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0018]
In order to solve the above problem, the image processing apparatus according to the present invention is characterized in that the halftone frequency determining unit counts a density change of each pixel of a document image to determine a halftone area pixel. Determining means, threshold setting means for storing a threshold value for determining the halftone frequency of the original image, and the halftone line for the halftone area pixel number calculated by the first halftone determining means or the original type automatic determining means It is characterized by comprising a halftone frequency determining means for determining the halftone frequency by comparing the ratio of the number of halftone area pixels calculated by the number determining means with the threshold value.
[0019]
According to the above invention, the halftone area pixel is determined by counting the density change of each pixel of the document image. Here, “counting the density change” includes, for example, counting the number of reversals of the document image, but is not particularly limited as long as the density difference of the document image can be counted. By counting the number of reversals of the original image, the halftone area pixels can be determined. Generally, a halftone dot with a low screen ruling has a small number of inversions, and a halftone dot with a high screen ruling has a large number of inversions.
[0020]
Therefore, by counting the density change (for example, the number of times of reversal) of the document image, the characteristic amount of the dots in the document image can be extracted, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0021]
An image processing apparatus according to the present invention is characterized in that, in order to solve the above-described problem, the threshold value setting means stores a plurality of threshold values.
[0022]
According to the above invention, the threshold value setting means stores a plurality of threshold values.
[0023]
Therefore, the determination of the halftone frequency is performed in multiple stages. As a result, the halftone frequency can be determined finely. This makes it possible to further perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0024]
In order to solve the above-mentioned problem, the image processing apparatus according to the present invention is configured such that the first halftone dot determining unit, the automatic document type determining unit, and the halftone frequency determining unit convert the input image data into binary data. And a number of inversions counting means for counting the number of inversions, which is the number of binary switchings in the binary data, and calculates the number of halftone dot pixels from the maximum value or the sum of the inversions. It is characterized by doing.
[0025]
According to the above invention, the halftone area pixels are calculated based on the maximum value of the number of reversals or the total sum of the number of reversals in the data obtained by binarizing the document image.
[0026]
Therefore, by counting the maximum value or the total sum of the number of inversions of the data obtained by binarizing the original image, it is possible to extract the characteristic amount of the halftone dot in the original image and determine the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0027]
In order to solve the above problem, the image processing apparatus according to the present invention, in the above configuration, further includes a filter processing unit that performs a filtering process on the input image data before the processing by the binarization processing unit. It is characterized by having.
[0028]
According to the above-described invention, before the original image is converted into the binary data, the original image is filtered. That is, the document image is subjected to image enhancement by the filter processing.
[0029]
Therefore, in the input image data, a density difference at a specific pitch, which is a characteristic of a halftone dot, becomes clear. Thus, the number of inversions can be counted more accurately.
[0030]
In order to solve the above-mentioned problem, the image processing apparatus according to the present invention is configured such that the reversal number counting means mainly performs a reversing operation on a block including a plurality of pixels including a pixel of interest, which is one pixel forming a document image. The number of inversions is counted in a scanning direction, a sub-scanning direction substantially perpendicular to the main scanning direction, and an arbitrary angle with respect to the main scanning direction different from the sub-scanning direction. And
[0031]
According to the above invention, counting of the number of inversions is performed in the main scanning direction, the sub-scanning direction, and the direction at an arbitrary angle.
[0032]
Therefore, when the original image is a halftone dot having a screen angle, or regardless of the installation direction of the original image, the number of reversals can be counted. Therefore, the halftone frequency can be accurately determined.
[0033]
In order to solve the above problem, the image processing apparatus according to the present invention is characterized in that the reversal number counting means includes a correction means for correcting the result of counting the number of reversals according to a condition for reading a document image. .
[0034]
According to the above invention, the correction means corrects the counting result of the number of inversions. The number of reversals changes according to the scanning speed, that is, the magnification in the sub-scanning direction. For example, even in the case of a document image in which the number of reversals in the main scanning direction and the sub-scanning direction is the same, the number of reversals in the sub-scanning direction increases during reduction or high-speed scanning such as pre-scanning. Conversely, during low-speed scanning such as during enlargement, the number of reversals in the sub-scanning direction is reduced. Thus, an error in the number of inversions occurs according to the scan speed.
[0035]
Therefore, if the number of inversions in each scanning direction is corrected according to the scanning speed, the error in the number of inversions is eliminated. As a result, the number of inversions can be counted accurately. This can eliminate errors in the number of inversions, especially when counting the number of inversions by pre-scanning at a speed faster than the normal scan and when counting the number of inversions in main scanning.
[0036]
In order to solve the above-described problems, the image processing apparatus according to the present invention may be configured such that the correction unit sets the number of pixels for which the number of inversions is counted in all directions in which the number of inversions is counted by the number of inversions counting unit. It is characterized by being equal.
[0037]
According to the above invention, the correction is performed so that the number of pixels obtained by counting the number of inversions in all of the main scanning direction, the sub-scanning direction, and the oblique direction is the same. The number of pixels for which the number of inversions is counted may be different between the case where the number of inversions is counted obliquely with respect to the block pixels and the case where the number of inversions is counted in the main scanning direction and the sub-scanning direction. That is, the number of inversions counted in each direction may not be counted under the same condition. Therefore, the maximum value of the number of reversals may not be the maximum value of the number of reversals in the document image.
[0038]
Therefore, the number of inversions counted in all directions is counted under the same condition. As a result, it is possible to always select the maximum value of the number of reversals in the document image. Thus, the halftone frequency of the document image can be accurately determined.
[0039]
In order to solve the above problems, the image processing apparatus according to the present invention performs the processing by the halftone dot determining means or the automatic document type automatic determining means on the outward path when reading an original image by the image input means. The processing by the dotted line number determining means is performed at the time of returning when reading the document image by the image input means.
[0040]
According to the above-described invention, when the scanner serving as the image input unit travels in the forward direction, the halftone dot in the document image or the document type is determined, and when the scanner returns, the halftone frequency is determined. That is, a single scan determines a halftone dot in a document image or a document type, and determines a halftone frequency. Note that the image input means refers to a scanner using a light source, a mirror, a lens, a CCD, or the like.
[0041]
Therefore, the place where two scans should be performed can be made one scan. As a result, the time required to determine the halftone dots and halftone frequency of the original image can be reduced. Thus, when applied to an image forming apparatus, the copy speed can be reduced.
[0042]
In order to solve the above-mentioned problem, the image processing apparatus according to the present invention is characterized in that the halftone frequency determining unit determines the halftone frequency using image data within a predetermined range in a document image. .
[0043]
For example, image data such as the edge of a document image often does not represent features of the document image. In addition, image data within the approach distance range of the reading range of the original image has a low reading speed, so that the reliability of the image data is poor.
[0044]
According to the above invention, the halftone frequency determining unit determines the halftone frequency by using image data within a predetermined range in the document image. In other words, predetermined data of the document image, that is, the halftone frequency of the region excluding the end of the document image and the run distance range of the reading range of the document image is determined.
[0045]
Therefore, it is possible to determine the halftone frequency of the portion representing the feature of the document image and the highly reliable portion of the image data. As a result, the halftone frequency of the document image can be accurately determined.
[0046]
In order to solve the above-mentioned problems, an image processing apparatus according to the present invention includes: an image data in a range in which a document image is read in order to perform processing by the halftone dot determining means or the automatic document type determining means; It is characterized in that image data in a range in which a document image is read in order to perform processing by the determination means are substantially the same.
[0047]
According to the above invention, the range of the document image read when performing the processing by the halftone frequency determining means, the document type automatic determining means, and the halftone frequency determining means is substantially equal.
[0048]
Therefore, the halftone frequency is determined using the image data in the same range of the document image. Thus, the halftone frequency of the document image can be accurately determined.
[0049]
In order to solve the above-mentioned problems, the image processing apparatus according to the present invention may be arranged such that the control means includes a step of determining whether the original image includes halftone dots by the first halftone dot determining means, If it is determined that the original is a dot original, the first halftone determining means or the original type automatic determining means determines the halftone area by the halftone frequency determining means, rather than a first threshold for determining a halftone area pixel. It is characterized in that control is performed so as to increase the second threshold value for determining a pixel.
[0050]
According to the above invention, when the original image includes a halftone dot or is a halftone original, the second threshold is larger than the first threshold. In other words, the level for determining the halftone area pixels is set higher in the halftone frequency determining unit than in the first halftone determining unit or the original type automatic determining unit.
[0051]
Therefore, for example, a pixel having a small number of inversions (that is, a halftone dot having a low screen ruling) is determined to be a halftone dot pixel by the halftone dot frequency determining unit, even though the first halftone dot judging unit judges it as a halftone dot pixel. Will not be. Thereby, the halftone frequency of the document image can be determined.
[0052]
In order to solve the above-mentioned problems, the image processing apparatus according to the present invention may be arranged such that the control means determines that the halftone dot is not included in the document image by the halftone dot determination means, When the original image is determined to be a non-halftone original by the determining means, control is performed so that the processing by the halftone frequency determining means is not performed.
[0053]
According to the above invention, the control means controls to determine the halftone frequency only when the original image includes a halftone dot or when the original image is a halftone original.
[0054]
Therefore, the time required for determining the halftone dots and the halftone frequency of the document image can be reduced. Thus, when applied to an image forming apparatus, the copy speed can be reduced.
[0055]
An image processing apparatus according to the present invention includes any one of the image processing apparatuses described above in order to solve the above problems.
[0056]
According to the above invention, an image reading device of the present invention includes the image processing device of the present invention.
[0057]
Therefore, it is possible to provide an image reading apparatus that can extract the characteristic amount of the halftone dot in the document image and determine the halftone frequency with a simple configuration. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0058]
An image forming apparatus according to the present invention includes any one of the image processing apparatuses described above in order to solve the above-described problems.
[0059]
According to the above invention, an image forming apparatus of the present invention includes the image processing device of the present invention.
[0060]
Therefore, it is possible to provide an image forming apparatus capable of extracting a characteristic amount of a halftone dot in a document image with a simple configuration and determining a halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image. That is, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image free from moire while maintaining sharpness.
[0061]
An image processing method according to the present invention is directed to an image processing method for generating output image data for obtaining an output image by determining a halftone frequency of an original image from input image data in order to solve the above problem. Based on the calculation result of the halftone area pixels of the original image, a halftone dot determination process for determining whether or not the original image contains halftone dots, and the halftone area of the original image based on the calculation result of the halftone area pixel number of the original image. It is characterized by including a halftone frequency determining process for determining the number of dotted lines, and a control process for controlling the halftone frequency determining process according to a result of the halftone determining process.
[0062]
According to the invention described above, by calculating the halftone area pixels of the original image, it is determined whether or not the original image contains halftone dots. The halftone frequency of the original image is determined based on the original image. That is, it is not necessary to determine the halftone frequency by performing a complicated and enormous process of a circuit scale such as pattern matching and frequency analysis as in the related art.
[0063]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image contains halftone dots. Based on the calculation result of the halftone area pixels, the characteristic of the halftone dots in the original image is determined. By extracting the quantity, the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image. That is, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image without moire while maintaining sharpness.
[0064]
According to another aspect of the present invention, there is provided an image processing method for generating output image data for obtaining an output image by determining a halftone frequency of a document image from input image data. In the method, based on the calculation result of the halftone area pixel number of the original image, the document type automatic determination processing for determining whether the original image is a halftone original, and the calculation result of the halftone area pixel number of the original image And a control process for controlling the halftone frequency determination process in accordance with the determination result of the automatic document type determination process based on the halftone frequency determination process for determining the halftone frequency of the document image. .
[0065]
According to the above invention, by calculating the halftone area pixels of the original image, it is determined whether or not the original image is a halftone original, and if the original image includes halftone dots, the halftone area pixel is calculated based on the calculation result. Thus, the halftone frequency of the document image is determined. That is, it is not necessary to determine the halftone frequency by performing a complicated and enormous processing such as a circuit scale such as pattern matching and frequency analysis as in the related art. Note that the halftone original indicates, for example, a printed photograph or the like, and may include an original in which characters and halftone areas are mixed.
[0066]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image is a halftone original, and based on the calculation result of the halftone area pixels, the halftone dot in the original image is determined. The feature amount is extracted, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, thereby improving the image quality of the output image.
[0067]
A program according to the present invention causes a computer to execute any one of the above-described image processing methods in order to solve the above-described problems.
[0068]
According to the above-described invention, it is possible to provide a program that allows a computer to read and execute an image processing method capable of extracting a characteristic amount of a halftone dot in a document image and determining a halftone frequency with a simple configuration. Therefore, the image processing method according to the present invention can be general-purpose.
[0069]
In order to solve the above-mentioned problems, a recording medium according to the present invention is characterized in that the program is stored in a computer-readable manner.
[0070]
According to the above invention, it is possible to provide a recording medium capable of easily supplying a computer with a program of an image processing method capable of extracting a characteristic amount of a halftone dot in a document image and determining a halftone frequency with a simple configuration.
[0071]
BEST MODE FOR CARRYING OUT THE INVENTION
[Embodiment 1]
One embodiment of the present invention will be described below with reference to FIGS.
[0072]
The image forming apparatus of the present invention is a tandem-type digital color copying machine 1 as shown in FIG. In recent years, tandem-type color image forming apparatuses have become mainstream, in combination with the fact that internal apparatuses have been miniaturized and assembled (unitized) to be relatively inexpensive. First, the tandem type digital color copying machine 1 will be described.
[0073]
FIG. 2 is a sectional view of a main part of the digital color copying machine 1 to which the tandem system is applied. The digital color copying machine 1 includes image forming stations 200K, 210C, 220M, and 230Y, a paper feeding unit 240, a transport belt 250, and a fixing device 260.
[0074]
The image forming station includes image forming stations 200K, 210C, 220M, and 230Y of C, M, Y, and K (C: cyan, M: magenta, Y: yellow, K: black).
[0075]
In FIG. 2, the image forming station includes a photoconductor, a charger, a writing optical system, a developing device, and a cleaning device. Taking the image forming station 200K as an example, the drum-shaped photoconductor 201K rotates in the direction of arrow D in the figure, and around it, the charger 202K, the writing optical system 203K, the developing device 204K, and the cleaning device 205K are arranged at least in the order of rotation. Is arranged.
[0076]
In the digital color copying machine 1, the surface of the photoconductor between the charger 202K and the developing device 204K is irradiated with laser light from the writing optical system 203K so that a K electrostatic latent image is formed on the photoconductor 201K. Has become.
[0077]
Image forming stations 210C, 220M, and 230Y equivalent to the image forming station 200K centering on the photoconductor 201K are arranged side by side along a sheet conveying belt 250 that is a sheet conveying unit. The paper transport belt 250 is in contact with the photoreceptor between the developing device and the cleaning device of each image forming station, and applies a transfer bias to a surface (rear surface) of the paper transport belt 250 that contacts the back side of each photoreceptor side. Transfer rollers 206K, 216C, 226M, and 236Y. Each image forming station has the same configuration except that the toner color inside the developing device is different. The same configuration applies to C, M, and Y.
[0078]
In the digital color copying machine 1 having the configuration shown in FIG. 2, the image forming process is performed as follows. First, in each of the image forming stations 200K, 210C, 220M, and 230C, the photoconductors 201K, 211C, 221M, and 231Y are charged by the chargers 202K, 212C, 222M, and 232Y, and then the writing optical systems 203K, 213C, 223M, and 233Y. , An electrostatic latent image corresponding to the image of each color to be formed is formed.
[0079]
Next, the electrostatic latent images are developed by the developing devices 204K, 214C, 224M, and 234Y to form toner images. The developing devices 204K, 214C, 224M, and 234Y are developing devices that perform development with K, C, M, and Y toners, respectively. The toner images of each color formed on the four photoconductors 201K, 211C, 221M, and 231Y are Stacked on paper.
[0080]
The sheet is sent from the sheet feeding unit 240 to the sheet conveying belt 250 in the direction of arrow E in the figure at the same time as the image formation on the photoconductors 201K, 211C, 221M, and 231Y.
[0081]
The sheet held on the sheet conveying belt 250 is conveyed, and the transfer of the toner image of each color is performed at a contact position (transfer section) with each of the photoconductors 201K, 211C, 221M, and 231Y. The toner images on the photoconductors 201K, 211C, 221M, and 231Y are transferred onto paper by an electric field formed by a potential difference between the transfer bias applied to the transfer rollers 206K, 216C, 226M, and 236Y and the photoconductors.
[0082]
The recording paper on which the four color toner images are superimposed after passing through the four transfer units is conveyed to the fixing device 260, where the toner is fixed, and is discharged to a discharge unit (not shown).
[0083]
Further, residual toner remaining on each photoconductor without being transferred at the transfer unit is collected by the cleaning devices 205K, 215C, 225M, and 235Y.
[0084]
In the digital color copying machine 1 shown in FIG. 2, the image forming units are arranged in the order of K, C, M, and Y from the upstream side to the downstream side in the sheet conveying direction, but the order is not limited. Instead, the order of the colors may be set arbitrarily.
[0085]
Further, as described above, each image forming station has the same configuration except that the toner color inside the developing device is different. Therefore, the rotation speed of the developing device of each image forming station is also set to the same rotation speed.
[0086]
Next, the digital color copying machine 1 will be described in detail. The digital color copying machine 1 is a digital color copying machine including a color image processing apparatus to which the configuration of the image processing apparatus of the present invention is applied, as shown in a block diagram in FIG. First, the configuration of the digital color copying machine will be described.
[0087]
As shown in FIG. 1, a digital color copying machine (image forming apparatus) 1 includes a color image input device 2, a color image processing device 3, a color image output device 4, and an operation panel 5.
[0088]
The color image input device (image input device) 2 includes a scanner unit having, for example, a CCD (Charge Coupled Device). Then, the reflected light image from the original image is read by a CCD as input image data in the state of analog signals of RGB (R: red, G: green, B: blue) and input to the color image processing device 3.
[0089]
The color image processing apparatus 3 includes an A / D converter 6, a shading corrector 7, a first halftone determiner 8, a halftone frequency determiner 9, an input tone corrector 10, a segmentation processor 11, a color corrector. 12, a black generation and under color removal section 13, a spatial filter processing section 14, an output gradation correction section 15, and a gradation reproduction processing section (halftone generation) 16. Then, the RGB analog signals input from the color image input device 2 are sent in the above order and output to the color image output device 4 as CMYK digital color signals. In this manner, the color image input device 2 and the color image output device 4 are connected to the color image processing device 3 to constitute the digital color copying machine 1 as a whole.
[0090]
The A / D (analog / digital) converter 6 converts an RGB analog signal output from the color image input device 2 into a digital signal.
[0091]
The shading correction unit 7 performs a process of removing various types of distortion generated in the illumination system, the imaging system, and the imaging system of the color image input device with respect to the digital RGB signals input from the A / D conversion unit 6. Further, it converts the RGB reflectance signals into density signals.
[0092]
The first halftone determining section 8 determines whether or not a halftone area is included in the input document image. In addition, the first halftone dot determination unit also calculates the number of halftone dot pixels in the document image. Then, according to the determination result of the halftone dot area, a signal for changing the parameter of the halftone frequency determining unit 9 is output to the control unit 17 in the subsequent stage.
[0093]
The halftone frequency determining unit 9 determines the halftone frequency using the parameter changed by the signal from the control unit 17.
[0094]
The present invention particularly relates to a first halftone determining unit, a halftone frequency determining unit, and a control unit, and a configuration example thereof will be described later in detail.
[0095]
The input tone correction unit 10 adjusts the color balance of the RGB signals, and at the same time, performs image quality adjustment processing such as removal of background density and contrast.
[0096]
The region separation processing unit 11 performs a process of separating each pixel in the input image into any of a character region, a halftone dot region, and other (photograph or the like) regions from the RGB signals. Then, based on the result of the separation, an area identification signal indicating to which area the pixel of interest belongs is sent to the subsequent color correction unit 12, black generation and under color removal unit 13, spatial filter processing unit 14, and gradation reproduction process. Output to the unit 15. Further, the input signal output from the input tone correction unit 10 is output as it is to the subsequent color correction unit 12.
[0097]
The color correction unit 12 performs a process of removing color turbidity based on the spectral characteristics of CMY (C: cyan, M: magenta, Y: yellow) color materials including unnecessary absorption components, in order to realize faithful color reproduction. Things.
[0098]
The color correction unit 12 converts a RGB signal (or an input CMY signal obtained by inverting a complementary color of the RGB signal) into an output CMY (C: cyan, M: magenta, Y: yellow) signal for faithful color reproduction. Perform conversion processing.
[0099]
The black generation and under color removal unit 13 includes a black generation unit and an under color removal unit. The black generation unit performs black (K) generation processing based on the CMY signal and the region identification signal that have been color-converted by the color correction unit 12. The under color removal unit performs a process of subtracting the amount of under color calculated from the black signal from the CMY signal to generate a new CMY signal. As a result, the CMY three-color signals are converted into CMYK four-color signals.
[0100]
As an example of the black generation processing, there is a method (general method) of performing black generation using skeleton black. In this method, the input / output characteristics of the skeleton curve are y = f (x), the input data is C, M, Y, and the output data is C ′, M ′, Y ′, K ′, UCR (Under Color). Assuming that the (Removal) rate is α (0 <α <1), the black generation and under color removal processing is represented by the following equation (1).
[0101]
K ′ = f {min (C, M, Y)}
C ′ = C−αK ′
M ′ = M−αK ′ (1)
Y ′ = Y−αK ′
The spatial filter processing unit 14 performs a spatial filter process by a digital filter on the area identification signal and the image data of the CMYK signal input from the black generation / under color removal unit 13 based on the area identification signal. That is, the processing is performed so as to correct the spatial frequency characteristic and prevent the output image from being blurred and the graininess from deteriorating.
[0102]
The output tone correction unit 15 performs an output tone correction process of converting a density signal or the like into a dot area ratio which is a characteristic value of the color image output device 4. Finally, the tone reproduction processing unit 14 performs a tone reproduction process (halftone generation) that separates the image into pixels and performs processing so that each tone can be reproduced.
[0103]
Similarly to the spatial filter processing unit 14, the tone reproduction processing unit 16 performs a predetermined process on the image data of the CMYK signal based on the region identification signal.
[0104]
For example, in order to enhance the reproducibility of a black character or a color character in particular, the image region separated into characters by the region separation processing unit 11 is emphasized at a high frequency by sharp enhancement processing in the spatial filter processing 14 by the spatial filter processing unit 14. The amount is increased. At the same time, the tone reproduction processing unit 16 performs binarization or multi-value processing on a high-resolution screen suitable for reproduction of high-frequency components.
[0105]
The image area separated into the halftone area by the area separation processing section 11 is subjected to a low-pass filter processing for removing an input halftone component in the spatial filter processing section 14. Then, the output tone correction unit 15 performs an output tone correction process of converting a signal such as a density signal into a halftone dot area ratio which is a characteristic value of the color image output device 4. Then, a tone reproduction process (halftone generation) for finally separating the image into pixels and performing processing so that each tone can be reproduced is performed. Further, with respect to the region separated into other regions by the region separation processing unit 11, binarization or multi-value processing is performed on a screen that emphasizes tone reproducibility.
[0106]
As described above, the image data of the CMYK signal that has been subjected to each processing in each unit of the color image processing apparatus 3 is temporarily stored as image output data in a storage unit (not shown), read out at a predetermined timing, and Input to the image output device. The image output device 4 outputs image data to a recording medium (for example, paper), and includes, for example, a color image output device using an electrophotographic system or an inkjet system, but is particularly limited. is not. The above processing is controlled by a CPU (Central Processing Unit) not shown.
[0107]
As described above, the color image processing device (image processing device) 3 performs predetermined image processing on input image data input from the color image input device 2, and outputs image output data input to the color image output device 4. And
[0108]
The operation panel 5 includes a setting button for setting an operation mode of the digital copying machine 1 (not shown), input means such as a numeric keypad, and a display screen including a liquid crystal display.
[0109]
The image processing of the first halftone determining unit 8, the halftone frequency determining unit 9 and the control unit 17 in the digital color copying machine 1 is performed, for example, as shown in the flowchart of FIG. That is, first, a pre-scan is performed prior to a main scan (S1). Next, the first halftone dot determination unit 8 calculates the number of halftone dot pixels and determines the presence or absence of halftone dot pixels from the obtained input signal data (S2, S3). At this time, if there is no halftone dot area pixel, the halftone frequency determining unit 9 does not perform the halftone frequency determination (S4), terminates the process, and enters the main scanning process. On the other hand, if there is a halftone area pixel, the control unit 17 changes the parameter of the halftone frequency determining unit 9 (S5). Subsequently, the halftone frequency determining unit 9 determines the halftone frequency using the changed parameters (S6), and starts the main scan. In the processing after the main scan, the area separation parameter, the filter coefficient, the dither pattern, and the like at the subsequent stage are selected according to the halftone frequency determination result, and the optimum processing is performed. As a result, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image free from moire while maintaining sharpness. The main scan is a scan for performing optimal image processing based on the information of the document image obtained in S1 to S6.
[0110]
Also, needless to say, the image processing of the present invention is not limited to the above processing. FIG. 3 illustrates a configuration in which the prescan is performed, but a configuration in which the prescan is not performed may be employed. In this case, the input image data may be stored in a storage unit such as an image memory, and a series of image processes may be performed after performing a halftone frequency determination process using the image data.
[0111]
Next, the first halftone dot determining unit 8 and the halftone frequency determining unit 9, which are characteristic parts of the present invention, will be described in detail.
[0112]
(1) First halftone dot determination unit
As shown in FIG. 4, the first halftone dot determining unit 8 includes a character area determining unit 18, a first halftone dot area determining unit 19a, a first determining unit 20a, and a comprehensive determining unit 21.
[0113]
The character area determination unit 18 determines whether or not the shading-corrected RGB signal is a character area pixel. Similarly, the first halftone area determination unit 19a determines whether or not the shading-corrected RGB signal is a halftone area pixel.
[0114]
As shown in FIG. 5, the first halftone dot area determination unit 19a includes a binarization processing unit 27, an inversion count unit 28, a maximum value selection unit 29, an inversion count determination unit 30, and a parameter setting unit 31a. ing.
[0115]
The binarization processing unit 27 binarizes the input image data using a value such as an average value calculated from pixel values in the mask including the target pixel as a threshold.
[0116]
The number-of-reversals counting section 28 includes a main scanning direction counting section 32 and a sub-scanning direction counting section 33. Then, the number of inversions of the pixels in the mask in each of the main scanning direction and the sub-scanning direction is counted.
[0117]
The maximum value selection section 29 selects the maximum value of the result of the number of inversions in the inversion number counting section 28. The maximum value selected here is compared with the parameter (first threshold) set in the parameter setting unit 31 by the number-of-inversions judgment unit 30. Thus, it is determined whether the pixel is a dot area pixel or a non-dot area pixel. Then, the determination signal is input to the first determination unit 20a.
[0118]
The first determination unit 20a determines whether each pixel of the document image is a halftone dot region, a character region, or a pixel region based on the determination results by the character region determination unit 18 and the first halftone region determination unit 19a. It is determined whether it is neither.
[0119]
The comprehensive determination unit 21 determines the presence or absence of a halftone dot in a document image.
[0120]
Next, the processing in each unit constituting the first halftone dot determination unit 8 will be described in detail.
[0121]
As the determination method of the character area determination unit 18, for example, the method of Japanese Patent Application No. 2001-30212 filed by the present applicant can be used. Hereinafter, this method will be briefly described.
[0122]
First, in an nxm block pixel (m and n are natural numbers) including a target pixel, a minimum density value and a maximum density value in the main scanning direction are calculated, and a maximum density difference is calculated from the minimum density value and the maximum density value. . Next, the sum of absolute values of the density differences between adjacent pixels (total density busyness) is calculated. The maximum density difference and the maximum density difference threshold, and the total density busyness and the total density busyness threshold are compared, and the maximum density difference is larger than the maximum density difference threshold, and the total density busyness is the total density busyness threshold. If larger, it is determined to be a character / halftone dot area.
[0123]
Further, the calculated total density busyness is compared with a value obtained by multiplying the maximum density difference by a character / halftone determination threshold value. If the total density busyness is smaller, it is determined that the area is a character area.
[0124]
This is characterized by the fact that the maximum density difference is large in the character area and the total density busyness increases with it, but the density change is smaller than the halftone area, so the total density busyness is smaller than the halftone area. It was used.
[0125]
The determination method of the character area determination unit is such that the above-described processing is similarly performed on the pixels in the sub-scanning direction, and the area of the pixel of interest is determined based on each determination result.
[0126]
Next, as a determination method of the first halftone dot region determination unit 19a, for example, it can be performed as shown in a flowchart of FIG. That is, first, in the binarization processing unit 27, the pixels in the mask are binarized using a value such as an average value calculated from the pixel values in the mask including the target pixel as a threshold (S7). Next, the number of inversions (that is, the number of points changed from “0” to “1” and “1” to “0”) from the binarized pixels in the mask is counted by the inversion number counting unit 28 in the main scanning direction. And counting in the sub-scanning direction (S8). Subsequently, the maximum value selection unit 29 selects the maximum value (MaxRev) of the counted number of inversions (S9), and the inversion number determination unit 30 determines the maximum value (MaxRev) of the number of inversions and the parameter setting unit 31a. Is compared with the first threshold value (TH1: for example, 4) (S10). If the maximum value of the number of reversals (MaxRev) is larger than the first threshold value (TH1), it is determined that the pixel is a halftone dot pixel (S11), and the maximum value of the number of reversals (MaxRev) is set to the first threshold value (TH1). If not, it is determined that the pixel is a non-dot area pixel (S12).
[0127]
Next, a determination method of the first determination unit 20 will be described. The first determination section 20 determines the area of each pixel of the document image based on the determination results of the character area determination section 18 and the first halftone area determination section 19a. In the first determination unit 20a, for example, as shown in Table 1 below, it is determined whether each pixel of the document image is a pixel of a halftone dot region, a character region, or another region. . In the present embodiment, since the purpose is to determine whether or not a halftone dot region is included in the original image, the first halftone region determination unit 19a as shown in Table 1 shows the original image. It is determined that the halftone dot region is included in the target pixel ("1"), and the character region determination unit 18 determines that the target pixel of the document image also includes the character region (" In the case of 1 "), the target pixel is set to be determined to be a halftone dot area. That is, when the first halftone area determination unit 19a determines that the pixel is a halftone area pixel, the first determination unit 20a also determines that the pixel is a halftone area pixel.
[0128]
[Table 1]

[0129]
As described above, the first determination unit 20a determines which region each pixel belongs to, as in the determination example from the region determination signal shown in Table 1 (here, the halftone region pixel Therefore, when the character area pixel determination: 1 and the halftone area pixel determination: 1, the pixel is determined to be a halftone pixel.)
[0130]
Each pixel determined by the first determination unit 20a is counted up by the character area pixel counter 22 and the halftone area pixel counter 23a. Further, the divider 25 calculates the ratio of the number of halftone pixels to the total number of pixels. The second determination unit 26 determines that there is a halftone dot in the document image if the ratio of the number of halftone pixels to the total number of pixels is larger than a predetermined value (eg, 0.05), and if the ratio is less than the predetermined value, it indicates that A determination signal indicating that there is no halftone dot is output to the control unit 17.
[0131]
(2) Halftone frequency determination unit
Next, the halftone frequency determining unit 9 will be described. As shown in FIG. 7, the halftone frequency determining unit 9a includes a second halftone area determining unit 34, a halftone area pixel counter 23b, a halftone frequency determining unit 35, and a threshold setting unit 36. Further, the control unit 17 changes a parameter of a parameter setting unit 31b, which will be described later, according to the determination result of the first halftone dot determination unit 8 described above.
[0132]
The second halftone dot area determination section 34 includes a binarization processing section 27, an inversion count section 28, a maximum value selection section 29, an inversion count determination section 30, and a parameter setting section 31b. That is, the configuration of the second halftone dot area determination unit 34 is the same as the configuration of the first halftone dot area determination unit 19a shown in FIG. Further, the second halftone dot area determination unit 34 determines whether or not the pixel of interest is a halftone dot area pixel, similarly to the first halftone dot area determination unit 19a.
[0133]
The parameter setting unit 31b stores a processing parameter (second threshold value) for the second halftone dot area determination unit 34. Based on this parameter, it is determined whether the pixel of interest is a halftone pixel or a non-halftone pixel. This parameter is changed by the control unit 17 according to the determination result of the first halftone dot determination unit 8 described above. For example, when the first halftone determination unit 8 determines that a halftone dot exists in the document image, the control unit 17 changes the parameter of the parameter setting unit 31b. In this case, the parameter before the change of the parameter setting unit 31b is set to a value larger than that before the change, such as "2" and the parameter after the change to "4". As a result, when the number of inversions is small (that is, the number of halftone dots is low), it is possible not to determine the pixel as a halftone dot area pixel.
[0134]
The halftone frequency determining unit 35 calculates the first halftone area pixel number (scr1) calculated by the halftone area pixel counter 23a of the first halftone determining unit 8 and the halftone area pixel counter of the halftone frequency determining unit 9a. By comparing the ratio with the second halftone area pixel number (scr2) calculated in 23b with the threshold (TH2) set in the threshold setting unit 36, the halftone frequency is determined.
[0135]
It should be noted that the halftone dot area judgment unit 19a of the first halftone dot judgment unit 8 and the second halftone dot area judgment unit 34 may have a common configuration. Further, the dot area pixel counter 23a in FIG. 4 and the dot area pixel counter 23b in FIG. In this case, the control unit 17 may change the parameters of the parameter setting unit 31a in FIG. 5 when the first halftone determination unit 8 determines that there is a halftone area pixel in the document image.
[0136]
Next, another configuration of the halftone frequency determining unit 9 will be described. As shown in FIG. 8, the halftone frequency determining unit 9b includes a character area determining unit 18, a second halftone area determining unit 34, a first determining unit 20b, a character area pixel counter 22, a halftone area pixel counter 23c, A halftone frequency determining unit 35 and a threshold setting unit 36 are provided. The halftone frequency determining unit 9b in FIG. 8 has a configuration similar to that of the first halftone determining unit 8 shown in FIG. 4 and a configuration similar to the halftone frequency determining unit 9a in FIG. However, unlike the first halftone determining unit 8 of FIG. 4, the halftone frequency determining unit 9b does not include the total pixel number counter 24, the divider 25, and the second determining unit 25, and the halftone frequency determining unit 35 And a threshold setting unit 36. The halftone frequency determining unit 9b is different from the halftone frequency determining unit 9a in FIG. 7, in addition to the second halftone area determining unit 34, the character area determining unit 18, the first determining unit 20b, and the character area pixel. A counter 22 is provided. The control unit 17 is set so as to change the parameters of the second halftone dot area determination unit 34, similarly to the halftone frequency determination unit 9a.
[0137]
The shading-corrected RGB signals input to the halftone frequency determining unit 9b determine whether or not the pixel of interest is a character area pixel at the character area determining unit 18 and determine at the second halftone area determining unit 34 It is determined whether the pixel of interest is a halftone dot pixel. Note that the configuration of the second halftone dot region determination unit 34 may be the same as the configuration of the second halftone dot region determination unit 34 described with reference to FIG.
[0138]
From these determination results, the first determination unit 20b determines which region each pixel belongs to, as in the determination example shown in Table 1 described above, for example. Each pixel thus determined is counted up by the character area pixel counter 22 and the halftone area pixel counter 23c. The halftone frequency determining unit 35 determines a ratio between the number of halftone area pixels calculated by the first halftone determining unit 8 and the number of halftone area pixels calculated by the second halftone area determining unit 34 as a threshold setting unit. By comparing with the threshold value of 36, the halftone frequency is determined.
[0139]
The above-described image processing (particularly, image processing in the halftone frequency determining unit 9) of the first halftone determining unit 8, the halftone frequency determining unit 9, and the control unit 17 is, for example, a flowchart illustrated in FIG. It is performed as follows. That is, first, a pre-scan is performed prior to the main scan (S13). Next, the first halftone dot judging unit 8 calculates the first halftone dot area pixel number (scr1) and judges whether or not there is a halftone dot pixel from the obtained input signal data (S14, S15). At this time, if it is determined that there is no halftone dot in the document image, the halftone frequency determining unit 9 does not determine the halftone frequency (S16), terminates the process, and enters the main scan process. On the other hand, when it is determined that the document image has a halftone dot, the control unit 17 changes the parameter (second threshold value) of the parameter setting unit 31 of the halftone frequency determining unit 9 (S17). Subsequently, the second halftone area pixel number (scr2) is calculated by the second halftone area determination unit 34 and the halftone area pixel counter 23 using the changed parameter (second threshold value) (S18). ). Next, the halftone frequency determining unit 35 determines the ratio of the first and second halftone area pixel numbers (scr1 and scr2) (that is, the second halftone area pixel number / the first halftone area pixel number: scr2 /). scr1) is calculated (S19). Next, a ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers and a threshold value (TH2: for example, 0.25) set in the threshold value setting unit 36 of the halftone frequency determination unit 9 (threshold value of the threshold value) As an example, see FIG. 10 described later.) Is compared (S20). If the ratio (scr2 / scr1) of the number of pixels in the first and second halftone dot regions is larger than the threshold value (TH2), it is determined that the halftone dot has a high screen ruling (S21), and the main scanning process is started. On the other hand, if the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers is equal to or smaller than the threshold value (TH2), it is determined that the halftone dot is a low screen ruling (S22), and the main scanning process is started.
[0140]
In the processing after the main scan, the area separation parameter, the filter coefficient, the dither pattern, and the like at the subsequent stage are selected according to the halftone frequency determination result, and the optimum processing is performed. As a result, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image free from moire while maintaining sharpness. The main scan is a scan for performing optimal image processing based on the information of the document image obtained in S13 to S22.
[0141]
As described above, the halftone frequency determining unit 9 performs image processing using the feature that the halftone dot with a low ruling has a small number of inversions and the halftone with a high ruling has a large number of inversions. Therefore, it is possible to determine the halftone frequency with a simple configuration without performing pattern matching or complicated conversion processing as in the related art.
[0142]
The image processing shown in the flowchart of FIG. 9 can be performed, for example, as shown in the flowchart of FIG. The flowchart of FIG. 10 is an example in which the threshold setting unit 36 of the halftone frequency determining unit 9 includes a plurality of thresholds (TH3 to TH5). Steps S23 to S29 in the flowchart shown in FIG. 10 correspond to S13 to S19 in the flowchart shown in FIG. In the flowchart of FIG. 10, when the halftone frequency determining unit 35 calculates the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers (S29), then the first and second halftone dot areas are determined. The ratio of the number of dot area pixels (scr2 / scr1) is compared with a threshold (TH3: different from the above-described threshold (TH2)) set in the threshold setting unit 36 of the halftone frequency determining unit 9 (S30). . If the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers is larger than the threshold value (TH3), it is determined that the halftone dot has a high screen ruling (S31), and the main scanning process is started. If the ratio (scr2 / scr1) of the first and second halftone dot area pixels is equal to or less than the third threshold value (TH3), the ratio of the first and second halftone dot area pixel numbers (scr2 / scr1) The threshold value (TH4) set in the threshold value setting unit 36 is compared (S32). If the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers is larger than the threshold value (TH4), it is determined that the halftone dot has a medium frequency higher than the high frequency (S33), and the main scan is started. . If the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers is equal to or less than the threshold value (TH4), the ratio of the first and second halftone dot area pixel numbers (scr2 / scr1) is further reduced to: The threshold value (TH5) set in the threshold value setting unit 36 is compared (S34). If the ratio (scr2 / scr1) of the first and second halftone dot area pixel numbers is larger than the threshold value (TH5), it is determined that the halftone dot has a lower number of lines than the low number of lines (S35), and is equal to or less than the threshold value (TH5). If so, it is determined that the halftone dot has a low screen ruling (S36), and the main scanning process is started.
[0143]
The threshold value of the threshold value (TH3), the threshold value (TH4), and the threshold value (TH5) are not particularly limited as long as the condition of TH3>TH4> TH5 is satisfied. For example, TH3 = 0.40, TH4 = 0.25, and TH5 = 0.10.
[0144]
Further, in the processing after the main scan, an area separation parameter, a filter coefficient, a dither pattern, and the like are selected according to the halftone frequency determination result, and the optimum processing is performed. As a result, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image free from moire while maintaining sharpness.
[0145]
Further, by setting a plurality of threshold values of the threshold value setting unit 35 in the halftone frequency determining unit 9, it is possible to output the determination result of the document image step by step. Therefore, the more the threshold value is set, the more detailed the determination result can be obtained. As a result, image processing can be performed with settings according to the characteristics of the document image, and higher image quality can be achieved.
[0146]
Of course, the image processing of the present invention is not limited to the above processing. Although FIG. 10 illustrates the configuration in which the prescan is performed, a configuration in which the prescan is not performed may be employed. In this case, the input image data may be stored in a storage unit such as an image memory, and a series of image processes may be performed after performing a halftone frequency determination process using the image data.
[0147]
Next, the inversion number counting unit 28 will be described. As shown in FIG. 11, the number-of-reversals counting section 28 includes an oblique direction counting section 37a in addition to the main scanning direction counting section 32 and the sub-scanning direction counting section 33.
[0148]
The number-of-reversals counting unit 28 calculates the number of reversals from the binarized image in each of the main scanning direction, the sub-scanning direction, and the oblique direction. As shown by the broken line in the figure, n (n is an arbitrary natural number) oblique direction counting units 37a, 37b,... 37n are provided to calculate the number of inversions (in a plurality of directions) over a plurality of angles. The configuration may be as follows. Thus, the number of reversals can be counted regardless of whether the original image is a halftone dot having a screen angle or regardless of the installation direction of the original image. Therefore, the halftone frequency can be accurately determined.
[0149]
FIGS. 12A to 12D and FIGS. 13A to 13D show examples of counting of the number of inversions in each direction in the inversion number counting unit 28. In FIGS. 12 and 13, the block pixel size (sometimes referred to as a mask size) including the target pixel is 10 × 10.
[0150]
FIG. 12A shows an example of the counting method of the main scanning direction counting unit 32. a1, a2, a3,..., a10 inversion count result (Hrev1), b1, b2, b3,..., b10 inversion count result (Hrev2). Is counted to obtain the number of reversals in the scanning direction (Hrev1 to Hrev10).
[0151]
FIG. 12B shows an example of the counting method of the sub-scanning direction counting unit 33. Also in this case, similarly to the case of the main scanning direction counting unit 32, the number of inversion counts (Vrev1) of a1, b1, c1,..., J1, the number of inversions of a2, b2, c2,. The number of inversions in the sub-scanning direction is sequentially counted as a counting result (Vrev2), and the number of inversions in the sub-scanning direction (Vrev1 to Vrev10) is obtained.
[0152]
FIGS. 12C, 12D, and 13A to 13D show examples of the counting method of the oblique direction counting unit 37. FIG. 12C shows an example of a counting method in a 45 ° oblique direction. a3, b4, c5,..., h10 inversion count result (S1rev1), a2, b3, c4,..., i10 inversion count result (S1rev2), a1, b2, c3,. The number of inversions in the diagonal direction of 45 ° is sequentially counted, such as the result of counting the number of inversions in j10 (S1rev3), and the number of inversions in the diagonal direction of 45 ° (S1rev1 to S1rev5) is obtained.
[0153]
In the diagonal direction, the number of target pixels for counting the number of inversions in one line is different from eight pixels in S1rev1, nine pixels in S1rev2, and ten pixels in S1rev3, so that the counting result may be corrected. For example, the correction can be made as in the following equations (2) and (3). This correction may be performed by a correction unit (not shown).
S1rev1 ′ = S1rev1 × 10/8 (2)
S1rev2 ′ = S1rev2 × 10/9 (3)
As a result, the number of pixels for which the number of reversals is counted in all directions is the same. That is, the error of the number of inversions in each direction for counting the number of inversions is eliminated.
[0154]
FIG. 12D shows an example of a counting method in the oblique −45 ° direction. a8, b7, c6,..., h1 inversion count result (S2rev1), a9, b8, c7,..., i1 inversion count result (S2rev2), a10, b9, c8,. The number of determinations in the diagonal −45 ° direction is sequentially counted as in the count of the number of inversions of j1 (S2rev3), and the number of inversions in the diagonal −45 ° direction (S2rev1 to S2rev5) is obtained. In this case, the result of counting the number of inversions may be corrected, for example, as in Expressions (2) and (3), as in the case of the above-described oblique 45 ° (S1rev1 ′ to S1rev5 ′).
[0155]
FIG. 13A shows an example of a counting method in an oblique direction of about 72 °. a1, a2, a3, b4,..., d10 inversion count result (S3rev1), b1, b2, b3, c4,..., e10 inversion count result (S3rev2). The number of inversions in the 72 ° direction is counted, and the number of inversions in the diagonal 72 ° direction (S3rev1 to S3rev7) is obtained.
[0156]
FIG. 13B shows an example of a counting method in an oblique direction of about −72 °. In this case, similarly to the case of FIG. 13A, the result of counting the number of inversions of a10, a9, a8, b7,..., D1 (S4rev1), b10, b9, b8, c7,. The number of inversions in the diagonal −72 ° direction is sequentially counted, such as the result of counting the number of inversions (S4rev2), and the number of inversions in the diagonal −72 ° direction (S4rev1 to S4rev7) is obtained.
[0157]
FIG. 13C shows a counting method in a diagonal direction of about −22 °. a10, b10, c10, d9,..., j7 inversion count result (S5rev1), a9, b9, c9, d8,..., j6 inversion count result (S5rev2). The number of inversions in the direction of about −22 ° is counted, and the number of inversions in the oblique direction of about −22 ° (S5rev1 to S5rev7) is obtained.
[0158]
FIG. 13D shows a counting method in an oblique direction of about 63 °. a1, a2, b3, b4,..., e10 inversion count result (S6rev1), b1, b2, c3, c4,..., f10 inversion count result (S6rev2). The number of inversions in the direction of about 63 ° is counted, and the number of inversions in the oblique direction of about 63 ° (S6rev1 to S6rev6) is obtained.
[0159]
As in the examples of FIGS. 12 and 13, with respect to the diagonal direction, the number of inversions at an arbitrary angle is set to be counted. Which angle direction is to be counted may be set in advance in a combination or may be configured to be arbitrarily selectable. When selecting a combination, it is sufficient that the combination can be input from the operation panel 5 provided in the digital color copying machine 1. When the present invention is implemented using a computer system described later, a combination pattern may be displayed on an image display device such as a liquid crystal display, and a desired pattern may be selected using a keyboard or a mouse. .
[0160]
From the results of counting the number of inversions in each of the main scanning direction, the sub-scanning direction, and the oblique direction, the maximum value is selected by the maximum value selection unit, and the number of inversions determination unit is calculated. Is compared with the parameter.
[0161]
Next, an example of the halftone dot pattern and the counting of the number of inversions by the inversion counting method as shown in FIGS. 12 and 13 will be described with reference to FIGS. 14 and 15. In FIGS. 14A, 14B, 15A, and 15B, the direction of counting the number of inversions is the direction of FIGS. 12 and 13, that is, the main scanning direction (FIG. 12A). ), The sub-scanning direction (FIG. 12B), ± 45 ° oblique (FIGS. 12C and 12D), and approximately ± 72 ° oblique (FIGS. 13A and 13B). I do.
[0162]
The upper part of FIG. 14A shows an example of a halftone dot having a high screen ruling at a mask size of 10 × 10, and the lower part shows a table of the number of inversions in each direction at the halftone dot having a high screen ruling in the upper part. . In this case, the maximum value of the number of inversions is “9” in the oblique 45 ° direction (S1rev3), which is indicated by the arrow in the upper part of the figure and the shaded part in the lower part of the table.
[0163]
Similarly, FIG. 14B shows an example of a halftone dot having a low screen ruling, and the number of times of each direction inversion. In this case, the maximum value of the number of inversions is “4” in the diagonal ± 45 ° directions (S1rev2 to S1rev4, S2rev2 to S2rev4) indicated by the upper arrow in the drawing and the hatched portion in the lower table.
[0164]
In FIG. 14A and FIG. 14B, for example, if the threshold (second threshold) of the parameter setting unit 41 in FIG. FIG. 14A in which the value is “9” is determined to be a halftone dot, and FIG. 14B in which the maximum value of the number of reversals is “4” is determined to be a halftone dot.
[0165]
FIG. 15A shows an example of a halftone dot having a high screen ruling and the number of times of each direction inversion. In this case, the maximum value of the number of inversions is “6” in the diagonal direction of about 72 ° (S3rev1, S3rev5) indicated by the upper arrow direction in the figure and the hatched portion in the lower table.
[0166]
FIG. 15B shows an example of a low screen ruling halftone dot and the number of reversals in each direction. In this case, the maximum value of the number of inversions is “3” in the direction of about 72 ° (S3rev1, S3rev2), which is indicated by an oblique line in the upper arrow direction and the lower table in the figure.
[0167]
In the case of FIG. 15A and FIG. 15B, the maximum value is “2” in the counting results of the number of inversions in only the main scanning direction and the sub-scanning direction. For this reason, the difference in the number of inversions between the high line count and the low line count cannot be detected. However, if the number of inversions in the oblique direction is counted, a difference in the number of inversions can be detected in such a case. That is, as in the case of FIGS. 14A and 14B, assuming that the threshold value of the parameter setting unit 31 in FIG. 7 is “4”, the second halftone frequency determination unit 34 performs inversion. FIG. 15A in which the maximum value of the number of times is “6” is determined to be a halftone dot, and FIG. 15B in which the maximum value of the number of inversions is “3” is determined to be not a halftone dot.
[0168]
In this way, the counting of the number of reversals is performed not only in the main scanning direction and the sub-scanning direction but also in the direction of an arbitrary angle, so that the original image is a halftone dot having a screen angle, Even when an image is placed obliquely, it is possible to accurately detect the halftone frequency.
[0169]
In addition, the first halftone area determination unit and the second halftone area determination unit may have this configuration, and the first halftone area determination unit and the second halftone area determination unit may be common.
[0170]
Incidentally, the number of reversals changes according to the scanning speed, that is, the magnification in the sub-scanning direction. Therefore, even when the determination of the halftone frequency is performed based on the pre-scan data at a speed higher than the normal speed or when the main scan data is used, at the time of zooming, it is determined in the main scanning direction and the sub-scanning direction. The magnification changes.
[0171]
For example, FIG. 16B shows data obtained by performing double speed scanning (that is, 50% magnification) in the sub-scanning direction on image data obtained by scanning at a normal speed as shown in FIG. FIG. 16 (c) shows data obtained when the scanning is performed at 1/2 speed (that is, at a magnification of 200%). When comparing FIG. 16 (a) with FIG. 16 (b) or FIG. 16 (c), the number of inversions in the main scanning direction is the same in each case, but the number of inversions in the sub-scanning direction is different. The number of reversals in the sub-scanning direction can be corrected by the following equation (4).
Number of inversions after correction = number of inversions before correction × α × γ / β (4)
Here, α = magnification (%) / 100, β = mask size in the sub-scanning direction, and γ = sub-scanning direction size to be counted for inversion. The size of the sub-scanning direction in which the number of inversions is counted indicates the number of blocks in the sub-scanning direction in which the number of inversions is counted.
[0172]
That is, in the case of FIG. 16B, α = 0.5, β = 10, and γ = 10, which are values obtained by multiplying the number of inversions before correction by 0.5. The above equation (4) can be similarly applied to the correction of the number of inversions in the oblique direction. For example, in the case of 1/2 speed scanning in the sub-scanning direction (ie, magnification of 200%) at an oblique angle of about 72 ° in FIG. 13A, α = 2, β = 10, γ = 4, and the number of inversions before correction is 0. .8.
[0173]
As described above, by correcting the result of counting the number of inversions in each of the main scanning direction, the sub-scanning direction, and the oblique direction according to the scanning speed, the error in the sub-scanning direction is eliminated, and more accurate. The result is obtained. Therefore, it is possible to accurately determine the halftone frequency in the case of the configuration in which the halftone frequency is determined in the pre-scan which is faster than the normal scan, or in the case of the magnification in the configuration in which the determination is performed in the main scan.
[0174]
Note that both the first halftone area determination unit 19a and the second halftone area determination unit 34 may have this configuration, and the first halftone area determination unit and the second halftone area determination unit may have a common configuration. .
[0175]
The same applies to a case where an original image is read by an ADF (automatic original image reading apparatus) or the like and preprocessing is performed based on coarse data, that is, when the sampling conditions of the read data are changed.
[0176]
Next, another configuration of the halftone frequency determining unit 9 will be described. As shown in FIG. 17, the halftone frequency determining unit 9c may include a filter processing unit 38 in addition to the configuration of the halftone frequency determining unit 9a in FIG. The halftone frequency determining unit 9c includes a filter processing unit 38 at a stage preceding the second halftone region determining unit 34 that performs binarization processing of input data.
[0177]
The filter processing unit 38 performs image enhancement by filter processing before the binarization processing of the input signal. The filter coefficient in the filter processing unit 38 may be a coefficient for performing general differential filter processing such as a Laplacian filter, for example.
[0178]
As described above, the image processing is performed by the filter processing unit 38 before the binarization processing of the input signal, so that the density difference at a specific pitch, which is a characteristic of a halftone dot, becomes clear. As a result, the number of inversions can be counted more accurately.
[0179]
Note that both the first halftone area determination unit 19a and the second halftone area determination unit 34 may include a filter processing unit 38, and the first halftone area determination unit and the second halftone area determination unit are shared. It is good also as composition of.
[0180]
[Embodiment 2]
The following will describe another embodiment of the present invention with reference to FIGS. For the sake of convenience, members having the same functions as those described in the first embodiment are denoted by the same reference numerals, and description thereof is omitted.
[0181]
As shown in FIG. 18, a digital color copying machine (image forming apparatus) 40 of the present embodiment employs an automatic document type discriminating unit 41 instead of the first halftone dot discriminating unit 8 in the first embodiment. Things. In the first embodiment, the presence / absence of a halftone dot in a document image is determined by the first halftone determination unit 8, but in the present embodiment, the type of the document image is determined by the document type automatic determination unit 41.
[0182]
The document type automatic discrimination unit 41 determines whether the document image is a dot document or a non-dot document (for example, a text document, a photographic paper photo document, etc.). When the original image is determined to be a halftone original, the control unit 17 changes the parameter of the halftone frequency determining unit 9 (the parameter of the parameter setting unit of the second halftone region determining unit). Output an appropriate signal.
[0183]
That is, in the first embodiment, it is determined whether or not the document image includes a halftone area, and the parameter of the halftone frequency determining unit 9 is changed by the control unit 17 based on the determination result. On the other hand, in the present embodiment, it is determined whether or not the original image is a halftone image (printed photograph) or a character / halftone image (an image in which characters and halftone images are mixed). The parameter of the halftone frequency determining unit 9 is changed based on the result. The operation of reading a document image in the present embodiment will be described later.
[0184]
Next, the configuration of the document type automatic discrimination unit 41 will be described. As shown in FIG. 20, the document type automatic discrimination unit 41 has the same configuration as the first halftone dot judgment unit 8 shown in FIG. However, the first halftone determination unit 8 compares the ratio of the halftone area pixels to the total number of pixels with the parameter set in the second determination unit 26 to determine whether or not the original image contains halftone dots. Had been determined. On the other hand, the document type determination unit 41 determines whether or not the document image is a dot document using the ratio of the halftone area pixels to the total number of pixels.
[0185]
The document type automatic determination unit 41 can determine the document type by using, for example, an algorithm used for area separation.
[0186]
The configuration of the first halftone area determination unit 19b is substantially the same as the configuration of the first halftone area determination unit 19a of the first embodiment. The method of determining the number of pixels in the halftone area is almost the same. That is, as shown in FIG. 21, the first halftone dot area determination unit 19b includes a binarization processing unit 27, an inversion count unit 28, an inversion count total calculation unit 42, an inversion count determination unit 30, and a parameter setting unit. 31a. Compared with the configuration of the first halftone dot area determination section 19a shown in FIG. 5, the configuration is the same except that the maximum value selection section 29 in FIG.
[0187]
The inversion number total calculation unit 42 counts the number of inversions in the main scanning direction and the sub-scanning direction for the pixels in the mask including the target pixel. Then, of the counted results, a total sum of lines indicating a predetermined number of inversions or more is calculated. The calculated sum is compared by the inversion number determination unit 30 with the parameter a set in the parameter setting unit 31. Thus, it is determined whether the pixel of interest is a halftone area pixel or a non-halftone area pixel.
[0188]
As described above, based on the determination results of the character region determination unit 18 and the first halftone region determination unit 19b, the first determination unit 20 determines which region each pixel of the document image has (in this embodiment, It is determined whether the pixel belongs to an area, a character area, or another area (for example, a photographic paper photograph).
[0189]
Further, as described above, the comprehensive determination unit 21 includes a character area pixel counter, a halftone area counter, a total pixel number counter, a divider, and a fifth determination unit. Then, the divider calculates the pixel of the character area, the pixel of the halftone area, and the pixel of the other area with respect to the total number of pixels. Then, based on the result, the type of the document image is finally determined.
[0190]
Document images can be classified into, for example, character documents, halftone documents, character / halftone documents, character documents, and other documents. Here, the other document refers to a document in a case where the first determination unit determines that it is neither a halftone area nor a character area. For example, in the document image, the background and the photographic paper photographic area are determined as pixels in other areas. For this reason, in the document image, most of the pixels are often determined to be other area pixels. Therefore, for example, when the number of halftone area pixels or character area pixels is 1% or more of the total number of pixels, the original image can be classified as a halftone original or character original. Table 2 shows an example of such a document type classification method.
[0191]
[Table 2]

[0192]
In Table 2, original images are classified into four types (character originals, halftone originals, character / halftone originals, and other originals). However, the document type automatic discrimination unit 41 only needs to classify a halftone document or a non-halftone document. Therefore, an original having a halftone area of 1% or more (that is, a halftone original and a character / halftone original) can be determined as a halftone original, and an original having a halftone area of less than 1% can be determined as a non-halftone original. It may be set as follows.
[0193]
In this way, when the document type automatic discriminating section 41 determines that the document image is a halftone document, the subsequent halftone frequency determining section 9 performs a halftone frequency detection process.
[0194]
Next, the halftone frequency determining unit 9d according to the present embodiment will be described. As shown in FIG. 22, the halftone frequency determining unit 9d has substantially the same configuration as the halftone frequency determining unit shown in FIG. However, the halftone frequency determining unit 9d is different in that the second halftone region determining unit 34 includes a total number of inversion times calculating unit 42.
[0195]
The image processing of the document type automatic discriminating unit 41, the halftone frequency determining unit 9, and the control unit 17 in the digital color copying machine 40 is performed, for example, as shown in the flowchart of FIG. The image processing shown in FIG. 19 is substantially the same as the image processing shown in FIG. 3 described in the first embodiment. That is, first, the first scan (pre-scan) is performed prior to the main scan (S37). Next, the document type automatic determination section 41 determines whether or not the document image is a halftone document from the obtained input signal data (S38, S39). At this time, if it is determined that the original is not a halftone original, the halftone frequency determining section 9 does not perform halftone frequency determination (S40), terminates the process, and enters the main scanning process. On the other hand, if it is determined that the original is a halftone original, the parameters of the halftone frequency determining unit 9 are changed by the control unit 17 (S41). Next, a second scan for determining the halftone frequency is performed, and the halftone frequency determining unit 9 determines the halftone frequency using the changed parameter (S42), and enters the main scan process. In the processing after the main scan, the area separation parameter, the filter coefficient, the dither pattern, and the like at the subsequent stage are selected according to the halftone frequency determination result, and the optimum processing is performed.
[0196]
As described above, after the type of the document image is determined, each process according to the halftone frequency is performed, so that it is possible to obtain an output image without moire while maintaining sharpness. The main scan is a scan for performing optimal image processing based on the information of the document image obtained in S37 to S42.
[0197]
In the first scan, the number of pixels in each of the character area, the halftone dot area, and other areas is calculated. In the first scan, since the purpose is to determine the document type, the parameters set in the document type determination unit 41 are set so that any halftone dot can be detected.
[0198]
On the other hand, in the second scan, since the purpose is to detect the halftone frequency, the parameter set in the halftone frequency determination unit is changed by the control unit so that only the high halftone frequency can be detected. You. For example, when it is determined that the original is a halftone original, the parameter of the halftone frequency determining unit is changed to be larger than the parameter of the automatic document type determination unit.
[0199]
Here, the method of discriminating a document image by the document type automatic discrimination unit 41 and the judgment method of the halftone frequency determining unit 9 will be described in detail with specific examples. Here, as a result of binarizing the original image using the threshold value of the automatic original type discriminating unit 41 as the average density in the 7 × 7 mask including the target pixel, the pixels are changed from white to black or black to white. The number of inversions will be counted.
[0200]
FIGS. 23 (a) to 23 (c) show low to high lines such as 65 lines (FIG. 23 (a)), 100 lines (FIG. 23 (b)), and 200 lines (FIG. 23 (c)). It shows a result of binarization using an average density of a number of halftone dots as a threshold. The margin shows the result of counting the number of inversions for each of these results.
[0201]
Looking at these three results, 65 lines (FIG. 23 (a)), 100 lines (FIG. 23 (b)), and 200 lines (FIG. 21 (c)) show that the number of inversions increases as the number of lines increases. It can be seen that it has increased.
[0202]
FIG. 24 further shows the number of inversions of halftone originals having various numbers of lines with a mask size of 7 × 7 as shown in FIG. 23, and detects all pixels in which the number of inversions is 2 or more. An example is shown. As a result, it is understood that the number having the number of inversions equal to or more than the predetermined number of times (here, two or more times) is correlated with the halftone frequency. That is, as in the case of FIG. 23, the number of inversions increases as the number of lines increases.
[0203]
Therefore, if this property is used to make the set value of the number of inversions (set value of the parameter setting unit) at the time of halftone dot detection in the second scan larger than the set value in the first scan, the second scan In this case, a halftone dot with a low screen ruling is not detected, and only a halftone dot with a high screen ruling is detected.
[0204]
For example, in the first scan, the set value of the number of inversions is set to “1” because the purpose is the original type. As a result, in the first scan, one or more inversion times are all detected. That is, all halftone dots with a low screen ruling are also detected. On the other hand, in the second scan, since the purpose is to detect a predetermined halftone frequency or more, the set value of the number of inversions is set to “2”. As a result, a portion (mask pixel) where the number of reversals detected as a halftone dot in the first scan is “1” is not detected as a halftone dot in the second scan.
[0205]
Therefore, when the ratio of the number of pixels determined to be halftone dots in the second scan to the number of pixels determined to be halftone dots in the first scan (halftone pixel number ratio) is close to 100%, the high screen ruling Points. Conversely, if the ratio is close to 0%, it can be determined that the screen has a low screen ruling.
[0206]
FIG. 25 shows a graph in which the ratio of the number of halftone pixels in a halftone original having various numbers of lines is set to the vertical axis, and the halftone frequency is set to the horizontal axis. As is clear from FIG. 25, the halftone frequency and the halftone pixel number ratio are correlated. The halftone frequency can be roughly classified into three types: low frequency, medium frequency and high frequency. Although not shown, the number of median lines is further classified into the number of median lines from the high number of lines and the number of medium lines from the low number of lines as in the first embodiment, and the number of median lines is divided into four levels as a whole. It can also be classified.
[0207]
If the number of halftone dots can be divided into multiple stages in this manner, it becomes possible to set the coefficients of the subsequent region separation parameter / filter processing and the dither pattern of the halftone processing to optimal settings. Therefore, after optimizing the coefficients of the segmentation parameter / filter processing and the dither pattern of the halftone processing, the third scan, which is the final main scan (all image processing operation), is performed to obtain an optimal image with respect to the halftone frequency. Processing can be performed. As a result, since each process is performed in accordance with the halftone frequency, it is possible to obtain an output image free from moire while maintaining sharpness.
[0208]
In the above description, the character area determination unit and the halftone area determination unit are provided, and the case where neither the character area nor the halftone area is determined as the other area. A configuration is also possible in which the total density busyness is determined, and a photo region determining unit that determines whether or not the region is a photograph region based on these feature amounts is provided in parallel with the character region determining unit and the halftone region determining unit ( Embodiment 1).
[0209]
In the above description, the configuration in which the document type automatic discriminating unit 41, the halftone frequency determining unit 9, and the area separation processing unit are separately provided has been described. Next, a description will be given of a configuration in which each processing unit performs common processing, and each processing unit is configured by one circuit, and the processing is switched by the CPU.
[0210]
FIG. 26 shows an example of a configuration in which each processing unit of the document type automatic discrimination unit 41, the halftone frequency determination unit 9, and the area separation processing unit 11 is integrated (see FIG. 18). In this case, each processing unit has the following configuration.
[0211]
As described above, the document type automatic discriminating section 41 includes the character area determining section 18, the first halftone area determining section 19c, the first determining section 20, the character area pixel counter 22, the halftone area pixel counter 23g, and the total number of pixels. It comprises a counter 24, a divider 25, and a second determination unit 26.
[0212]
The halftone frequency determining unit 9 includes a character area determining unit 18, a first halftone area determining unit 19c, a first determining unit 20, a halftone area pixel counter 23g, a halftone frequency determining unit 35, and a threshold setting unit 36. You.
[0213]
The area separation processing section 11 includes a character area determination section 18, a first halftone area determination section 19c, and a first determination section 20. For example, an area identification signal of a document image is output according to the determination criteria in Table 2. You.
[0214]
The document type automatic determination unit 41 outputs a document type determination signal from the second determination unit 26.
[0215]
When the halftone frequency determining unit 9 determines that a halftone original is included based on the result of the automatic document type determination unit 41, the control unit 17 changes the value of the parameter setting unit when determining the halftone. You. The halftone frequency determining unit 35 outputs a halftone frequency determining result.
[0216]
In the present invention, the method of determining the document type is not limited to the algorithm of the region separation processing as described in the present embodiment, but is a method of creating and determining a histogram which is a general method. Is also good. In order to realize the present invention, together with these document type discriminating methods, a feature amount (for example, total density busyness or run length) of a halftone pixel is extracted, and a pixel in a halftone region is extracted using this feature amount. Any method may be used as long as the determination criterion for determining whether or not there is a document can be changed between when determining the document type and when determining the halftone frequency.
[0217]
If it is determined that the image is not a halftone image in the first scan, the second scan, that is, the halftone frequency detection process is skipped and the third scan is performed to reduce the time required for the second scan. Becomes possible. Further, as a method of shortening the time, there is a method of increasing the scan speed described below.
[0218]
Here, a scanner that reads at a resolution of 600 dpi (dot per inch) at 100 mm / s (third scan speed) will be described as an example. In this case, the speed other than the third scan, which is the main scan, that is, the speed of the first and second scans is 200 mm / s. In addition, since the timing of the image signal from the CCD is not changed and the scanning speed is doubled, the sub-scanning resolution is half the normal resolution (that is, 300 dpi).
[0219]
Since the first scan and the second scan are performed only for document type determination and halftone frequency detection, it is possible to obtain substantially the same document type determination and halftone frequency detection results at half the normal resolution. . Therefore, the accuracy of the document type determination and the halftone frequency detection is not reduced, and the time can be shortened by the increase in the scanning speed.
[0220]
Further, as another method for shortening the time required for document type discrimination and halftone frequency detection, there is a method of performing the second scan at the time of return (return) of the first scan. In this case, it is preferable that the image detected by the first scan and the image detected by the second scan have the same size and the same resolution (the same scanning speed). That is, it is preferable that the halftone frequency is determined using image data of the same range of the document image. Thus, the halftone frequency of the document image can be accurately determined.
[0221]
For this reason, when the second scan is performed at the time of the return of the first scan, as shown in FIG. It is preferable to provide an approach portion in addition to the reading range. The reason is as follows. In other words, the motor driving the scanner requires a certain period of time to rotate at a constant speed. Therefore, the image reading speed changes during the time until the rotation at the constant speed. If a document image is read while the image reading speed is changing, it is difficult to read desired image data. Therefore, by setting the time until the rotation at the constant speed is the approaching portion, it is possible to read the image properly. This makes it possible to accurately determine the halftone frequency of the document image. The second scanning is performed by controlling the approaching portion so as not to be used for dot image detection.
[0222]
Further, by adopting a configuration in which the first, second, and third scan lengths and the scan image range are switched according to the document size, the speed of an A4 document that is usually used more frequently than that of an A3 document is increased. As a method of detecting the document size, for example, a generally known method may be used in which a plurality of photoelectric sensors are provided inside the document table according to the document size and the presence or absence of light detection is detected. The third scan requires scanning of the size of the document image, but the first and second scans may be substantially equal in image size and may be smaller than the document size.
[0223]
As described above, in the first and second embodiments, the digital color copier (image forming apparatus) has been described. However, the image processing apparatus according to the present invention has a flatbed type as shown in FIG. It can also be realized as a scanner 50 (image reading device) or the like. The flatbed scanner 50 (image reading apparatus) shown in FIG. 27 does not have the area separating function (that is, the area identification signal) as compared with the digital color copying machines 1 and 40 (image forming apparatus) of FIG. 1 or FIG. Is not output), and the device configuration is substantially the same.
[0224]
The determination result of the image data / document type read by the flatbed scanner 50 (image reading device) and the determination result of the halftone frequency are transmitted to an image processing server or a printer via a network, or to a SCSI (Small Computer System). The data is input to the computer via an interface such as an interface or a USB (Universal Serial Bus). The image processing device of the image reading device may include the input tone correction processing unit 10 and the area separation processing unit 11 shown in FIG. 1 or FIG.
[0225]
According to the present invention, the image processing method may be recorded on a computer-readable recording medium on which a program to be executed by a computer is recorded.
[0226]
As a result, it is possible to provide a portable recording medium on which a program for performing the image processing method is recorded.
[0227]
In the present invention, as the recording medium, a memory (not shown) such as a ROM itself, which is processed by a microcomputer, may be a program medium, or may be a program medium. Alternatively, a program reading device may be provided as an external storage device, and the program medium may be readable by inserting a recording medium therein.
[0228]
In any case, the stored program may be configured to be accessed and executed by a microprocessor, or in any case, the program may be read, and the read program may be stored in a microcomputer. The program may be downloaded to a program storage area that is not stored, and the program may be executed. It is assumed that this download program is stored in the main unit in advance.
[0229]
Here, the program medium is a recording medium configured to be separable from the main body, such as a tape system such as a magnetic tape or a cassette tape, a magnetic disk such as a floppy disk or a hard disk, or a CD-ROM / MO / MD / DVD. Disc system of optical disk, card system of IC card (including memory card) / optical card, or mask ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory, etc.) May be a medium that fixedly carries the program, including
[0230]
Further, in the present embodiment, since the system configuration is such that a communication network including the Internet can be connected, a medium that carries the program fluidly so that the program can be downloaded from the communication network may be used. When the program is downloaded from the communication network as described above, the download program may be stored in the main device in advance, or may be installed from another recording medium.
[0231]
The recording medium is read by a program reading device provided in a digital color copying machine or a computer system, so that the above-described image processing method is executed.
[0232]
The computer system includes an image input device such as a flatbed scanner, a film scanner, and a digital camera, a computer that performs various processes such as the above-described image processing method by loading a predetermined program, and a CRT display that displays a processing result of the computer. -It is composed of an image display device such as a liquid crystal display and a printer that outputs the processing results of the computer to paper or the like. Further, a modem or the like is provided as communication means for connecting to a server or the like via a network.
[0233]
The present invention is not limited to the embodiments described above, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention.
[0234]
【The invention's effect】
As described above, the image processing apparatus according to the present invention determines the halftone frequency of the original image from the input image data to generate output image data for obtaining the output image. It is characterized by comprising a halftone frequency determining means for identifying the halftone frequency of the document image based on the calculation result of the halftone area pixel number.
[0235]
Therefore, it is possible to extract the characteristic amount of the halftone dot in the document image with a simple configuration and determine the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0236]
As described above, in the image processing apparatus according to the present invention, the halftone frequency determining unit determines whether or not the original image contains halftone dots based on the calculation result of the halftone area pixel number of the original image. One halftone determining means, a halftone frequency determining means for determining the halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image, and a halftone frequency determining means according to the determination result of the first halftone determining means. And control means for controlling the halftone frequency determining means.
[0237]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image includes halftone dots, and based on the calculation result of the halftone area pixels, the halftone dots in the original image are determined. This has the effect of extracting the feature amount and determining the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0238]
As described above, in the image processing apparatus according to the present invention, the halftone frequency determining unit determines whether or not the original image is a halftone original based on the calculation result of the halftone area pixel number of the original image. Document type automatic discriminating means, based on the calculation result of the number of halftone dot pixels of the document image, a halftone frequency determining means for determining the number of halftone dots of the document image, and according to the determination result of the document type automatic discriminating means, Control means for controlling the halftone frequency determining means.
[0239]
Therefore, it is possible to determine whether or not the original image is a halftone original, thereby extracting the characteristic amount of halftone dots in the original image, and determining the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0240]
As described above, the image processing apparatus according to the present invention is such that the halftone frequency determining unit counts the density change of each pixel of the original image, thereby determining the halftone region pixel, Threshold value setting means for storing a threshold value for determining the halftone frequency of the original image; and halftone frequency determining means for the halftone area pixel number calculated by the first halftone determining means or the original type automatic determining means. It is characterized by including a halftone frequency determining means for determining the halftone frequency by comparing the calculated ratio of the halftone area pixel numbers with the threshold value.
[0241]
Accordingly, by counting the density change (for example, the number of inversions) of the original image, the feature amount of the halftone dots in the original image can be extracted, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0242]
As described above, the image processing apparatus according to the present invention is characterized in that the threshold value setting means stores a plurality of threshold values.
[0243]
Therefore, the determination of the halftone frequency is performed in multiple stages. As a result, there is an effect that the halftone frequency can be determined finely. This makes it possible to further perform image processing suitable for the determined halftone frequency, thereby providing an effect of improving the image quality of the output image.
[0244]
As described above, in the image processing apparatus according to the present invention, the first halftone dot determining unit, the document type automatic determining unit, and the halftone frequency determining unit determine the binary value of the input image data as binary data. Conversion processing means, and inversion number counting means for counting the number of inversions, which is the number of binary switchings in the binary data, and calculating the number of halftone dot area pixels from the maximum value or the sum of the number of inversions. Features.
[0245]
Therefore, by counting the maximum value or the total sum of the number of inversions of the data obtained by binarizing the original image, it is possible to extract the characteristic amount of the halftone dots in the original image and determine the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0246]
As described above, the image processing apparatus according to the present invention is configured such that, in the above-described configuration, the image processing apparatus further includes a filter processing unit that performs a filtering process on the input image data before the processing by the binarization processing unit. Features.
[0247]
Therefore, in the input image data, a density difference at a specific pitch, which is a characteristic of a halftone dot, becomes clear. Thereby, there is an effect that the number of inversions can be counted more accurately.
[0248]
As described above, in the image processing apparatus according to the present invention, the reversal number counting unit performs the main scanning direction on a block including a plurality of pixels including a pixel of interest, which is one pixel included in the document image. The number of inversions is counted in a sub-scanning direction that is substantially perpendicular to the main scanning direction and in a direction at an arbitrary angle with respect to the main scanning direction that is different from the sub-scanning direction.
[0249]
Therefore, when the original image is a halftone dot having a screen angle or regardless of the installation direction of the original image, the number of inversions can be counted. Therefore, there is an effect that the halftone frequency can be accurately determined.
[0250]
As described above, the image processing apparatus according to the present invention is characterized in that the number-of-reversals counting means includes the correction means for correcting the result of counting the number of reversals in accordance with a condition for reading a document image.
[0251]
Therefore, if the number of inversions in each scanning direction is corrected according to the scanning speed, the error in the number of inversions is eliminated. As a result, there is an effect that the number of inversions can be counted accurately. This has the effect of eliminating errors in the number of inversions, especially when counting the number of inversions by pre-scanning at a speed faster than the normal scan and when counting the number of inversions in main scanning.
[0252]
As described above, the image processing apparatus according to the present invention is configured such that the correction unit equalizes the number of pixels for which the number of inversions is counted in all directions in which the number of inversions is counted by the inversion number counting unit. Features.
[0253]
Therefore, the number of inversions counted in all directions is counted under the same condition. As a result, it is possible to always select the maximum value of the number of reversals in the document image. As a result, there is an effect that the halftone frequency of the document image can be accurately determined.
[0254]
As described above, the image processing apparatus according to the present invention is arranged such that the processing by the halftone dot determination means or the automatic document type automatic determination means is performed on the outward path when reading an original image by the image input means, and the halftone frequency determination means Is performed at the time of returning when reading an original image by the image input means.
[0255]
Therefore, there is an effect that a place where two scans should be performed can be made one scan. As a result, it is possible to reduce the time required for determining the halftone dots and the halftone frequency of the original image. As a result, when applied to an image forming apparatus, there is an effect that the copy speed can be reduced.
[0256]
As described above, the image processing device according to the present invention is characterized in that the halftone frequency determining means determines the halftone frequency using image data within a predetermined range in the document image.
Therefore, an effect is obtained that the halftone frequency of the portion representing the feature of the document image and the highly reliable portion of the image data can be determined. As a result, there is an effect that the halftone frequency of the document image can be accurately determined.
[0257]
As described above, the image processing apparatus according to the present invention, as described above, performs processing by the halftone dot determination unit or the original document type automatic determination unit. Is characterized in that the image data is substantially the same as the image data in the range in which the original image is read in order to perform
[0258]
Therefore, the halftone frequency is determined using the image data in the same range of the document image. As a result, there is an effect that the halftone frequency of the document image can be accurately determined.
[0259]
As described above, in the image processing apparatus according to the present invention, the control unit may be configured such that the document image includes a halftone dot by the first halftone dot determination unit, or the document image is a halftone dot original by the document type determination unit. Is determined by the halftone frequency determining unit as a halftone region pixel, rather than a first threshold value for determining the halftone region pixel by the first halftone determining unit or the document type automatic determining unit. Is controlled so as to increase the second threshold value.
[0260]
Therefore, for example, a pixel having a small number of inversions (that is, a halftone dot having a low screen ruling) is determined to be a halftone dot pixel by the halftone dot frequency determining unit, even though the first halftone dot judging unit judges it as a halftone dot pixel. Will not be. This has the effect that the halftone frequency of the document image can be determined.
[0261]
As described above, in the image processing apparatus according to the present invention, when the control unit determines that the halftone dot is not included in the document image by the halftone dot determination unit, or when the document type is automatically determined by the document type automatic determination unit, When an image is determined to be a non-halftone original, control is performed so that processing by the halftone frequency determining unit is not performed.
[0262]
Therefore, an effect is obtained that the time required for determining the halftone dots and the halftone frequency of the original image can be reduced. As a result, when applied to an image forming apparatus, there is an effect that the copy speed can be reduced.
[0263]
As described above, an image reading apparatus according to the present invention includes any one of the image processing apparatuses described above.
[0264]
Therefore, there is an effect that an image reading apparatus capable of extracting a feature amount of a halftone dot in a document image and determining a halftone frequency with a simple configuration can be provided. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0265]
As described above, an image forming apparatus according to the present invention includes any one of the image processing apparatuses described above.
[0266]
Therefore, there is an effect that an image forming apparatus capable of extracting a feature amount of a halftone dot in a document image and determining a halftone frequency with a simple configuration can be provided. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0267]
As described above, the image processing method according to the present invention is the image processing method of generating output image data for obtaining an output image by determining the halftone frequency of the original image from the input image data. Based on the calculation result of the halftone area pixels, a halftone dot determination process for determining whether or not the original image includes halftone dots, and a halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image. And a control process for controlling the halftone frequency determination process according to the result of the halftone frequency determination process.
[0268]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image includes halftone dots, and based on the calculation result of the halftone area pixels, the halftone dots in the original image are determined. This has the effect of extracting the feature amount and determining the halftone frequency. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0269]
Further, the image processing method according to the present invention, as described above, includes an image processing method for generating output image data for obtaining an output image by determining the halftone frequency of an original image from input image data. A document type automatic discrimination process for determining whether or not a document image is a dot document based on the calculation result of the pixel value of the dot region of the image, and a document image based on the calculation result of the pixel value of the dot region of the document image. And a control process for controlling the halftone frequency determination process in accordance with the determination result of the document type automatic determination process.
[0270]
Therefore, it is determined whether or not to determine the halftone frequency of the original image by determining whether or not the original image is a halftone original. Based on the calculation result of the halftone area pixels, the halftone dot in the original image is determined. Is extracted, and the halftone frequency can be determined. This makes it possible to perform image processing suitable for the determined halftone frequency, and thus has the effect of improving the image quality of the output image.
[0271]
A program according to the present invention causes a computer to execute any one of the image processing methods described above.
[0272]
Therefore, an advantage is provided in that a program can be provided that allows a computer to read and execute an image processing method capable of extracting the characteristic amount of a halftone dot in a document image and determining the halftone frequency with a simple configuration. Therefore, there is an effect that the image processing method according to the present invention can be made general-purpose.
[0273]
As described above, the recording medium of the present invention is characterized in that the above-mentioned program is stored in a computer-readable manner.
[0274]
Therefore, there is an effect that it is possible to provide a recording medium that can easily supply a computer with a program of an image processing method capable of extracting a characteristic amount of a halftone dot in a document image and determining a halftone frequency with a simple configuration.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration of a color image forming apparatus according to Embodiment 1 of the present invention.
FIG. 2 is a cross-sectional view illustrating a schematic configuration of a color image forming apparatus to which a tandem method is applied according to the first embodiment of the present invention.
FIG. 3 is a flowchart showing a flow of image processing in a first halftone dot determining unit, a halftone frequency determining unit, and a control unit of FIG. 1;
FIG. 4 is a block diagram illustrating a configuration of a first halftone dot determination unit in FIG. 1;
FIG. 5 is a block diagram illustrating a configuration of a halftone dot area determination unit in FIG. 4;
FIG. 6 is a flowchart showing a flow of processing in a halftone dot area determination unit in FIG. 5;
FIG. 7 is a block diagram illustrating a configuration of a halftone frequency determining unit in FIG. 1;
FIG. 8 is a block diagram showing another configuration of the halftone frequency determining unit of FIG. 1;
FIG. 9 is a flowchart showing a flow of determining the halftone frequency in FIG. 7 or FIG.
FIG. 10 is a flowchart showing a flow of another halftone frequency determination in FIG. 7 or FIG.
FIG. 11 is a block diagram showing a configuration of an inversion counter of FIG. 7 or 8;
12A and 12B are diagrams illustrating a method of counting the number of inversions in the number-of-inversions counting unit in FIG. 11, in which FIG. 12A illustrates an example of counting the number of inversions in a main scanning direction, and FIG. FIG. 12C shows an example of counting the number of inversions in the diagonal + 45 ° direction, and FIG. 12D shows an example of counting the number of inversions in the diagonal −45 ° direction. is there.
13A and 13B are diagrams illustrating a method of counting the number of inversions in the inversion number counting unit in FIG. 11; FIG. 13A illustrates an example of counting the number of inversions in the + 72 ° diagonal direction; FIG. 13C shows an example of counting the number of inversions in the direction of 72 °. FIG. 13C shows an example of counting the number of inversions in the direction of about −22 °. FIG. This is an example.
FIG. 14 is a diagram showing a result obtained by counting the number of inversions of a halftone dot pattern by the inversion number counting method of FIGS. 12 (a) to 13 (b).
FIG. 15 is a diagram showing the result of counting the number of times the halftone dot pattern is inverted by the inversion number counting method of FIGS. 12 (a) to 13 (d).
FIG. 16 is a diagram showing a counting result of the number of inversions when the scanning speed is changed for the same image data, and FIG. 16A is a counting result of the number of inversions at a normal scanning speed; 16 (b) is a diagram showing the result of counting the number of inversions in the case where the scan speed is twice the normal speed in the sub-scanning direction, and FIG. FIG. 10 is a diagram showing a counting result of the number of inversions in the case.
FIG. 17 is a block diagram showing another configuration of the halftone frequency determining unit of FIG. 1;
FIG. 18 is a block diagram illustrating a configuration of a color image forming apparatus according to Embodiment 2 of the present invention.
19 is a flowchart showing a flow of image processing in the document type automatic discrimination unit, the halftone frequency determining unit, and the control unit in FIG. 18.
20 is a block diagram illustrating a configuration of a document type automatic discrimination unit in FIG. 18;
FIG. 21 is a block diagram illustrating a configuration of a first halftone dot area determination unit in FIG. 20;
FIG. 22 is a block diagram illustrating a configuration of a halftone frequency determining unit in FIG. 18;
FIG. 23 is a diagram showing a result of binarizing image data of various halftones using the average density as a threshold, and FIG. 23A shows a result of binarizing 65 halftone dots. 23 (b) shows the result of binarizing 100 dots, and FIG. 23 (c) shows the result of binarizing 200 dots.
FIG. 24 is a graph showing the relationship between the halftone frequency and the number of inversions.
FIG. 25 is a graph showing a relationship between a halftone frequency and a halftone area pixel ratio.
26 is a block diagram in which the automatic document type determination unit, the halftone frequency determination unit, and the area separation processing unit of FIG. 16 are configured by one circuit.
FIG. 27 is a block diagram illustrating a configuration of an image reading apparatus according to an embodiment of the present invention.
FIG. 28 is a diagram illustrating a method for reducing the time required for image processing according to an embodiment of the present invention.
[Explanation of symbols]
1 Digital color copier (image forming apparatus)
8. First halftone dot determination unit (first halftone dot determination means, halftone frequency determining means)
9 Halftone frequency determining unit (halftone frequency determining means, halftone frequency determining means)
17 control unit (control means, halftone frequency identification means)
19 First Dot Area Determining Unit (Dot Area Determining Means)
27 Binarization processing unit (Binarization processing means)
28 Inversion frequency counting unit (inversion frequency factor means)
35 Halftone frequency determining unit (halftone frequency determining means)
36 threshold setting part (threshold setting means)
38 Filter processing unit (filter processing means)
40 Digital color copier (image forming apparatus)
41 Document Type Automatic Discrimination Unit (Document Type Automatic Discrimination Means, Halftone Frequency Identification Unit)
50 Flatbed scanner (image reading device)

Claims (21)

In an image processing apparatus that generates output image data for obtaining an output image by determining the halftone frequency of the original image from the input image data,
An image processing apparatus comprising: a halftone frequency determining unit that identifies the halftone frequency of a document image based on a calculation result of the number of halftone pixels in a document image. The halftone frequency identification means,
First halftone determining means for determining whether or not the original image contains halftone dots based on the calculation result of the number of halftone dot pixels of the original image;
A halftone frequency determining unit that determines the halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image,
2. The image processing apparatus according to claim 1, further comprising control means for controlling said halftone frequency determining means in accordance with a result of determination by said first halftone determining means. The halftone frequency identification means,
Document type automatic discrimination means for determining whether or not the document image is a dot document, based on the calculation result of the number of pixels in the dot area of the document image,
A halftone frequency determining unit that determines the halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image,
2. The image processing apparatus according to claim 1, further comprising control means for controlling said halftone frequency determining means in accordance with a result of determination by said document type automatic determining means. The halftone frequency determining means,
A dot area determining unit that determines a halftone area pixel by counting the density change of each pixel of the original image;
Threshold setting means for storing a threshold for determining the halftone frequency of the original image,
By comparing the ratio of the number of halftone area pixels calculated by the halftone frequency determination unit to the number of halftone area pixels calculated by the first halftone determination unit or the original type automatic determination unit with the threshold, The image processing apparatus according to claim 2, further comprising: a halftone frequency determining unit configured to determine a halftone frequency. The image processing apparatus according to claim 4, wherein the threshold setting unit stores a plurality of thresholds. The first halftone determination unit, the document type automatic determination unit, and the halftone frequency determining unit include:
Binarization processing means for converting input image data into binary data;
Inversion number counting means for counting the number of inversions, which is the number of binary switchings in the binary data,
4. The image processing apparatus according to claim 2, wherein the number of halftone dot pixels is calculated based on the maximum value or the total sum of the number of inversions. 7. The image processing apparatus according to claim 6, further comprising a filter processing unit that performs a filtering process on the input image data before the processing by the binarization processing unit. The reversal number counting means is configured to perform, in a main scanning direction and a sub-scanning direction substantially perpendicular to the main scanning direction, a block including a plurality of pixels including a pixel of interest, which is one pixel forming a document image. 7. The image processing apparatus according to claim 6, wherein the number of inversions is counted in a direction at an arbitrary angle with respect to the main scanning direction different from the main scanning direction. 7. The image processing apparatus according to claim 6, wherein the reversal number counting unit includes a correction unit that corrects a result of counting the number of reversals according to a condition for reading a document image. The image processing apparatus according to claim 9, wherein the correction unit equalizes the number of pixels for which the number of inversions is counted in all directions in which the number of inversions is counted by the inversion number counting unit. The process by the halftone dot determination means or the document type automatic determination means is performed on the outward path when reading the document image by the image input means,
The image processing apparatus according to claim 2, wherein the process performed by the halftone frequency determining unit is performed at the time of returning when reading an original image by the image input unit. The image processing apparatus according to claim 2, wherein the halftone frequency determining unit determines the halftone frequency by using image data within a predetermined range of the document image. The image data of the range for reading the document image for performing the processing by the halftone dot determining means or the document type automatic determining means, and the image data of the range for reading the document image for performing the processing by the halftone frequency determining means are 13. The image processing apparatus according to claim 12, wherein the image processing apparatuses are substantially the same. The control means includes: when the first halftone determination unit determines that the original image includes halftone dots, or when the original type determination unit determines that the original image is a halftone original,
A second threshold value for determining a halftone dot pixel by the halftone frequency determining means is smaller than a first threshold value for determining the halftone dot pixel by the first halftone dot determining means or the automatic document type determining means. The image processing apparatus according to claim 2, wherein the control is performed so as to increase the value. The control unit is configured to determine whether the original image does not include a halftone dot by the halftone determination unit or to determine that the original image is a non-halftone original by the original type automatic determination unit. The image processing apparatus according to claim 2, wherein control is performed such that processing by a halftone frequency determining unit is not performed. An image reading apparatus comprising the image processing apparatus according to claim 1. An image forming apparatus comprising the image processing apparatus according to claim 1. An image processing method for generating output image data for obtaining an output image by determining a halftone frequency of an original image from input image data,
Based on the calculation result of the halftone area pixels of the original image, a halftone dot determination process for determining whether or not the original image contains halftone dots, and a halftone line of the original image based on the calculation result of the halftone area pixel number of the original image. Halftone frequency determination processing for determining the number,
A control process for controlling the halftone frequency determining process according to a result of the halftone determining process. An image processing method for generating output image data for obtaining an output image by determining a halftone frequency of an original image from input image data,
Document type automatic discrimination processing for determining whether or not the document image is a dot document based on the calculation result of the number of pixels in the dot area of the document image,
A halftone frequency determining process for determining the halftone frequency of the original image based on the calculation result of the halftone area pixel number of the original image,
A control process for controlling the halftone frequency determination process in accordance with the determination result of the document type automatic determination process. A program for causing a computer to execute the image processing method according to claim 18. A recording medium storing the program according to claim 20 in a computer-readable manner.

Images