JPS62295567A - Estimating method for halftone picture of binary picture - Google Patents

Abstract

PURPOSE:To attain excellent recovery of the edge part by selecting an optimum scanning opening at each picture element through a prescribed operation among plural kinds of scanning openings set in advance. CONSTITUTION:Plural kinds of scanning openings are set as shown in figure, and a scanning opening as large as possible and whose estimate picture element is close to the center so long as no density change is recognized in the scanning opening. Thus, the estimated halftone picture is very close to the halftone picture from the original halftone picture.

Description

Detailed Description of the Invention 3. Detailed Description of the Invention [Field of Industrial Application] This invention provides a binary image that can satisfactorily estimate an original halftone image from a binary image displayed in pseudo-halftone. This invention relates to a halftone image estimation method for images.

[Prior Art] Currently, many of the output devices in practical use, such as display devices and printing devices, are only able to express information in two values: white and black.

Density pattern method (luminance pattern method), dither method, etc. are known as methods for expressing halftones in a pseudo manner using such an output device.

Both the density pattern method and the dither method are types of area gradation methods, and express halftone images by changing the number of dots recorded within a fixed area (matrix).

The density pattern method, as shown in Figure 25 (b), is a method of recording multiple dots in a portion corresponding to one pixel of the document using a threshold matrix, and the dither method, as shown in Figure 25 (b), This is a method in which a portion of the document corresponding to one pixel is recorded as one dot. As shown in the figures, binarized output data is obtained. This output data pseudo-expresses a halftone image using binary values of white and black.

By the way, if the original halftone image (corresponding to the input data in Fig. 25) can be estimated from such a binary pseudo halftone image, then this pseudo halftone image can be used to This processing is convenient because it is possible to create a binary image with good image quality.

In the case of a density pattern image, once the pattern level arrangement is known, the image can be immediately restored to a halftone image. However, the resolution is low compared to the amount of information.

On the other hand, a dithered image has a higher resolution than a density pattern image in terms of the amount of information, but it is difficult to restore the original halftone image.

In addition, when estimating a halftone image in this way, the halftone image is estimated without taking into account human visual characteristics or the type of image, so the characteristics of the image are not utilized.
Image quality cannot be improved sufficiently. If visual characteristics are also taken into account, it is possible to get the image even closer to the original halftone image.

Therefore, the present invention solves these conventional problems, and is an intermediate method of a binary image that can satisfactorily estimate the original halftone image from a binary image (for example, a binary dithered image). This paper proposes a tonal image estimation method.

[Means for Solving the Problems] In order to solve the above-mentioned problems, in the present invention, in a binary image created by a dither matrix, a plurality of scanning apertures are set for one type among a plurality of types, and the estimation is performed. A unique scanning aperture that satisfies a predetermined condition is selected for each pixel of the halftone image to be scanned, and the halftone image is estimated based on the number of white pixels or the number of black pixels within the selected scanning aperture. This is a characteristic feature.

[Operation] One image is processed under conditions such that in the low spatial frequency region, a large scanning aperture is used to express the gradation, and in the high spatial frequency region, the small scanning aperture is used to express the gradation. By doing so, it is possible to estimate a halftone image that matches human visual characteristics.

In addition, the binary image within the scanning aperture is compared with the image obtained by converting the halftone image created based on the number of white pixels or the number of black pixels within the scanning aperture into a binary image using a dither matrix for each aperture. By looking at the matching of the patterns of these two binary images, a halftone image including edge portions is estimated. This improves the restorability at the edge portion.

[Embodiments] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

Here, we first introduce 4X as one of the systematic binary dither methods.
An example will be explained in which a Bayer matrix of No. 4 is used as a threshold matrix.

FIG. 1 is a diagram showing an example of a binary dither image for explaining the present invention. (B) is the original halftone image converted to digital data, (B) is a 4x4 Bayer binary dither threshold matrix, and (C) is the threshold matrix (
This is a binary dithered image of the original image (a) converted into a black and white binary image (binary dithered image) by (b).

Note that in the binary dither image shown in FIG. 1(c), the white level is indicated by a white outline.

The Bayer binary threshold matrix has a dither pattern in which dots are dispersed, as shown in Figure (B).

FIG. 2 shows an example of a plurality of types of scanning apertures (unit areas) having different aperture areas used in the present invention.

(a) has a size of 2 rows x 2 columns, (b) has a size of 2 rows x 4 columns, (c) has a size of 4 rows x 2 columns, and (d) has a size of 4
Apertures each having a size of rows and four columns are shown.

Furthermore, the number of openings corresponding to the size of the matrix is prepared. Therefore, as shown in FIGS. 3 to 6, four scanning apertures A, eight apertures B and C, and 16 apertures each having the same area are prepared.

For example, the apertures All shown in FIG. They are used by being superimposed so that the pixels to be
1, 2), the estimated pixels are located.

With each specific pixel region shown in FIGS. 3 to 6 fixed, the dithered image shown in FIG. If the value pixel levels are summed and the value obtained by multiplying the sum by a gain is used as the estimated value of the halftone image, estimated halftone images as shown in FIGS. 7 to 10 will be obtained.

FIG. 7 shows an estimated halftone image when the aperture A in FIG. 3 is used, and A11' in the same figure shows the case where the halftone image is estimated using the aperture All. Similarly, the openings A12' to D44' shown in FIGS. 7 to 10 are
This is a case where a halftone image is estimated using '44.

Incidentally, a method for obtaining the estimated halftone image shown in FIG. 10 D11' will be explained.

Now, as shown in FIG. 11, the aperture defined in FIG.
The position at the lower right intersection of the columns. (hereinafter referred to as [2°2]).

In this case, the pixels included in the opening Dll as shown in the figure are
It is desirable that each is fully included.

That is, it is preferable to prevent a certain pixel from being partially missing.

Next, the number of white pixels (or the number of black pixels may be used) in the area surrounded by this opening Dll is summed and the value is used as the estimated value of the halftone image. In this case, it becomes 7. Therefore, 1 row 1 column 11 (
The estimated value of 1,1) is 7.

Next, the aperture D11 is moved to the right by one pixel (in this case, one column), and the number of white pixels in the aperture Dll at (1, 2) is summed up to 7 in the same manner as described above. Such calculation processing is performed sequentially for all columns in the same column.

Then, when the first row opening is completed, move the opening Dll by one row to the next row (second row) so that the opening center is [3, 2]
Starting from the position, the halftone density estimation operation is sequentially executed in the same manner as described above.

By sequentially moving the aperture and executing such arithmetic processing up to the last row and last column, halftone image estimated values are obtained, and the halftone image estimation operation is completed. The result calculated in this way is the estimated halftone image D shown in FIG.
11'. In the figure, this mark indicates an area where halftone image processing cannot be performed because there is no corresponding dither image data.

Next, a method for obtaining an estimated halftone image using the aperture Bll shown in FIG. 4 will be described.

When the opening Bll is selected, the movement start position of the opening Bit is as shown in FIG. The total number of white pixels in this state is 2, and in order to match the area to the aperture in the second diagram, it is necessary to double the total value in the aperture B11, so the pixel level in the aperture B11 is 2×2. =4. In this case, the gain of aperture B (B11 to B24) is 2.
It is said that

Similarly, when the gain of each aperture shown in FIG. 2 is determined, A (AIl to A22) is 4 and C (CIl to C24) is 2.

If such a calculation is executed every time the aperture Bll is moved by one pixel, a halftone image shown in FIG. 8 can be obtained. 7th
9 and 9 can be considered in the same way, so the explanation thereof will be omitted.

Even when the aperture is fixed in this manner, a halftone image can be estimated satisfactorily.

Of course, with such a method, a binary dithered image (
Since the halftone image (d) is estimated from the figure (c)), it does not completely match the halftone image created from the original halftone image, as shown in FIG. 10 and the like.

However, except where the density level of the original halftone image changes rapidly, a halftone image that closely approximates the original halftone image is obtained.

By the way, human vision has a high ability to discriminate pixel level gradations in low spatial frequency regions (regions with few pixel level changes), and has a high ability to discriminate pixel level gradations in high spatial frequency regions (regions with many pixel level changes). It has the characteristic of only having the ability to discriminate.

Therefore, if a large aperture is used in the low spatial frequency region to express high gradation, and a small aperture is used in the high spatial frequency region to reproduce an image with high resolution, it is possible to It is possible to estimate a halftone image even better than the tone image estimate.

Furthermore, in order to satisfactorily restore the edge portion of the double image to a halftone image, it is sufficient to prevent the scanning aperture containing the estimated pixels from covering this edge portion.

To do this, a plurality of scanning apertures in which estimated pixel positions have been specified are prepared, and among the scanning apertures that do not cover the edge portions, a scanning aperture in which the estimated pixel is located in the center is selected as much as possible.

Such aperture selection is performed for each pixel of the dithered image, and if a dithered image is created based on the estimated halftone image obtained in this way, the image can be restored without damaging the edges. can.

Therefore, the present invention attempts to estimate a halftone image by taking into consideration the pixel level gradation discrimination ability and edge discrimination ability of humans.

The method of this invention will be specifically explained below.

This method assumes that digital binary images have already been stored in a storage means such as a memory, and sets a plurality of types of scanning apertures for these digital binary images.
Perform predetermined arithmetic processing on the value image, select one optimal one from the plurality of types of scanning apertures mentioned above for each pixel, and add up the number of white pixels or the number of black pixels within the selected scanning aperture,
The total value is taken as the M [fixed value] of the halftone image.

The predetermined calculation process uses an algorithm that selects a large scanning aperture in the low spatial frequency region (region with few pixel level changes) and a small scanning aperture in the high spatial frequency region (region with many pixel level changes). It will be done.

Furthermore, an aperture is selected such that the estimated pixel is located as much as possible in the center of the scanning aperture so that the edge portion of the estimated image does not overlap the edge of the scanning aperture.

Therefore, the basic idea of the present invention is to select a scanning aperture that is as large as possible, and in which the estimated pixel is close to the center, as long as no density change is observed within the scanning aperture.

For this reason, the scanning aperture selection order is basically D-+C+B+A as shown in FIG.

More specifically, aperture selection is performed in the following order.

D23→D32→D22→D33→D12→D43→D
31→D24→D34→D21→D42→D13→D4
1→D14→D44→D tt+ C21-+ B 2
3 Pass B12 → C32 → B22 → C22 → C31 → B13
→C12→Bit→B24→C41→B14→C42 Pass C11-+ B 21+ A 21→A12→A22→
When the All estimated pixel is (1, 1), the estimated pixel is at the edge, so there are four types of apertures used at this time. Specifically, as shown in FIG. 13, Dll→ Take the order of Bll → C1l → All.

FIG. 14 is an explanatory diagram of an estimation method when estimating the halftone image level of the pixel (1, 1).

Process (1) Here, first, Dll is selected as the scanning aperture.

Then, move the scanning aperture Dll to the initial position (/\) in Figure 1 (/\).
When superimposed in the same manner as shown in FIG. 11, it becomes as shown in FIG. 14 (a). The total number of white pixels within this scanning amount of 11011 is 7. Assuming that this total value of 7 is the average pixel level, each pixel is compensated with 7 as shown in (b). When the average pixel level image shown in (b) is binarized using the dither matrix shown in (c), a binary image as shown in (d) is obtained.

Here, comparing binary dithered images (a) and (d),
It's not the same pattern.

The fact that the binary dither images (A) and (D) do not have the same pattern means that there has been a change in pixel level. Therefore, in this case, the scanning aperture Dll is inappropriate as a selection aperture.

Since scanning aperture Dll was not selected in step (1),
Proceed to step (2).

Step (2) The scanning aperture selected in step (2) is C1l.

Then, when the selection opening Ctt is superimposed on the initial position of FIG. 1(c), the result is as shown in step (2) of FIG. 14. The total number of white pixels within this scanning aperture C1l is four. Assuming that 8, which is obtained by multiplying this total value 4 by gain 2, is the average pixel level, each pixel is compensated with 8 as shown in (b). The average pixel level image shown in (b) is the dither matrix shown in (c) (consisting of the second and third columns of the threshold matrix in FIG. 1 (b), that is, the threshold matrix within the scanning aperture C1l) When binarized with (d)
It will look like the one shown below.

Here, comparing binary dithered images (a) and (d),
It's not the same pattern.

The fact that the two patterns are different means that there has been a change in one pixel level. Therefore, in this case as well, the scanning aperture C1l is inappropriate as a selection aperture.

Since scanning aperture C1l was not selected in step (1),
Proceed to step (3).

Step (3) The scanning aperture selected in step (3) is Bll.

Then, when the selection amount TIBII is superimposed on the initial position of FIG. 1(C), the result is as shown in FIG. 14(A). The total number of white pixels within this scanning aperture Bll is 2. Assuming that 4, which is obtained by multiplying this total value by a gain of 2, is the average pixel level, each pixel is compensated with 4 as shown in (b). When the average pixel level image shown in (B) is binarized using the dither matrix shown in (C), it becomes as shown in (D).

Here, comparing binary dithered images (a) and (d),
Both patterns match, so that this scanning aperture Bl
It is assumed that there is no change in pixel level within l.

Note that if the patterns do not match even after passing through such steps sequentially, the minimum scanning amount [IA is assumed to be selected.

In this way, the scan amount 11BII is selected.

The total number of white pixels within the scanning amount nB11 is 2 (al-4ft).

Since the gain of the scanning aperture B11 is 2, the estimated image value to be obtained is 2X2=4. That is, the number of white pixels shown in step (3) (b) of FIG. 14 is used as is as the halftone image estimated value.

The scanning aperture and aperture selection order used in the estimation process at pixel (1, 2) are as follows.

012→D 11+ B 12→C12→B tt+
C11→A12→All The aperture selected at this time is B12, if only the results are shown.

When the above operations are performed for each pixel of the binary dithered image shown in FIG. 1(C), an estimated halftone image as shown in FIG. 15(B) is obtained. Incidentally, the scanning aperture used for each halftone image estimation will be explained as shown in FIG. If the first row, first column to seventh column are shown, (1, 1) of the halftone estimated image is Bll, and (1, 2) is B12.
, (1,3) is B12, (1,4) is B12, (1.5) is B12, (1,6) is B12, (1,7
) is Bll.

The estimated halftone image shown in FIG. 15 is estimated by using a large scanning aperture in areas where there are few pixel level changes, and by using a small scanning amount [1] in areas where there are many pixel level changes. Since it is estimated, it is in line with human visual characteristics.

Therefore, the estimated halftone image is extremely close to the halftone image from the original halftone image shown in FIG. 1(A).

Furthermore, when the patterns do not match, the one with the smaller number of mismatched pixels is selected between the two patterns to be compared, and when the number of mismatched pixels is the same, the scanning aperture whose estimated pixels are closer to the center is selected. Therefore, there is no fear that the edge portion of the image will overlap the edge of the scanning aperture.

Incidentally, in the above description, a case has been described in which a halftone image is estimated from a binary image, but by performing tone conversion, applying a filter, or enlarging/reducing the estimated halftone image, A new binary image can be obtained.

FIG. 16 is a flowchart showing the case of performing gradation conversion (gradation processing) on an estimated halftone image. The flowchart shown in FIG. In this method, a new binary image is obtained using a threshold matrix for the halftone image.

As the gradation conversion characteristic, the one shown in FIG. 17 can be considered. In the figure, fl and f2 are tone conversion characteristic curves, the horizontal axis of which is the input, and the vertical axis of which is the output.The numbers shown in the figure are the density levels.

Fig. 18 (a) is a halftone image obtained by converting the gradation of the image shown in Fig. 15 (a) using the f1 characteristic shown in Fig. 17;
The halftone image (C) is a halftone image that has been tone-converted using the f2 characteristic shown in the figure, and (C) is a binary image that has been converted into a binary image using the Bayer binary dither matrix described above for the image shown in (A). This is a binary image obtained by binarizing the image shown in ). (c), (
As is clear from d), due to the difference in tone conversion characteristics, 2
It can be seen that the value images are significantly different.

FIG. 19 is a flowchart showing the case where the estimated halftone image is filtered. The flowchart shown in FIG.
Filtering the halftone image estimated by this invention,
A new binary image is obtained using a threshold matrix for the filtered halftone image.

Examples of filter characteristics are shown in FIG. (A) is a bypass convolution filter, and (B) is a low-pass convolution filter.

When the estimated halftone image shown in Fig. 15 (a) is filtered with the characteristics shown in Figs. 20 (a) and (b), bypasses as shown in Figs. 21 (a) and (b) are obtained , a low-pass halftone image is obtained. When these halftone images are binarized using the dither matrix shown in FIG. is obtained.

FIG. 22 is a flowchart showing the case of enlarging/reducing an estimated halftone image. The flowchart shown in FIG. A new binary image is obtained using a threshold matrix. As a method of enlarging/reducing, for example, an interpolation method is used.

FIG. 23(a) shows the halftone image shown in FIG. 15(a) using Nearest Neighbor 7, which is the simplest of the interpolation methods. (B) is a halftone image enlarged by 1.25 times using the Nearest Neighborhood method, and (B) is a halftone image reduced to <0.75 times.

When these halftone images are converted into binary dither using the dither matrix shown in FIG. A value image is obtained.

As the dither matrix, a matrix having a threshold value as shown in FIG. 24(b) can be used. When such a matrix is used, even when using the same halftone image level, A slightly different binary image is obtained. In other words, if the dither matrix shown in the figure (b) is used for the halftone image in the figure (a), a dithered image as shown in the figure (c) is obtained. It will be done.

Note that when estimating a halftone image from the above-mentioned binary image, the binary image is preferably a binary dithered image or a binary density pattern image, and particularly preferably a binary dithered image.

When using a binary dithered image, a binary dithered image using a systematic binary dithering method is preferable to random dithering or conditional dithering so that one threshold value is entered into each aperture with the maximum area, and A distributed binary dithered image in which the threshold value is evenly distributed even in the aperture with the smallest area is preferable, and a Bayer type binary dithered image in which the threshold value is completely distributed is particularly preferable.

In the above explanation, a halftone image is obtained by scanning one pixel at a time, but the present invention is not limited to this, and two or more pixels may be scanned at a time.

Furthermore, in the above description, the case where there are four types of apertures is illustrated, but the type of apertures is not limited. The size of the opening is not limited to the illustrated one, and any size can be used.

[Effects of the Invention] As explained above, according to the present invention, a plurality of types of scanning apertures are set, and an optimal scanning aperture is selected for each pixel by a predetermined calculation from among the plurality of types of scanning apertures. This is how it was done.

In this case, in the present invention, a large scanning aperture is used in the low spatial frequency region to express a high gradation, and a small scanning aperture is used in the high spatial frequency region to reproduce an image with high resolution. Therefore, it is possible to estimate a halftone image even better than the halftone image estimation values shown in FIGS. 7 to 10.

Furthermore, in this invention, since the scanning aperture is selected such that the estimated pixel is located at the center of the scanning aperture, the restoration of the edge portion is good. It has the characteristics of being able to

Once such a halftone image is obtained, various image processing such as gradation conversion, enlargement/reduction, etc. can be performed based on the halftone image.

[Brief explanation of drawings]

Fig. 1 is an explanatory diagram when obtaining a binary dither image from an original halftone image, Fig. 2 is a diagram showing multiple types of apertures, and Fig. 3
Figures 7 to 6 are diagrams showing the constant halftone image position l used in multiple types of apertures, and Figures 7 to 1θ are diagrams showing estimated halftone images obtained when these apertures are used. , FIG. 11 and FIG. 12 are diagrams showing the relationship between dithered images and apertures, FIG. 13 is a diagram showing an example of the aperture selection order, and FIG. 14 is a description of the halftone image estimation processing process. Figure (a)
16 is a diagram showing an example of an estimated halftone image obtained by the present invention, FIG.
The figure is a flowchart showing gradation conversion, Figure 17 is a diagram showing gradation conversion characteristics, and Figure 18 is 2fIi' due to gradation conversion.
FIG. 19 is a flowchart showing filtering, FIG. 20 is a diagram showing filter characteristics, and FIG.
Figure 1 shows the binarization process by filtering, Figure 2 shows the binarization process by filtering.
Figure 2 is a flowchart showing enlargement/reduction, Figure 23 is a diagram showing binarization processing by enlargement/reduction, and Figure 24 is a flowchart showing enlargement/reduction.
FIG. 25 is a diagram showing the relationship between the estimated halftone image shown in the figure and a dither image when dither matrices with different threshold values are used, and FIG. 25 is a diagram showing a conventional binarization method. (b) 3466531121 line 912161.410 8 5 3 1 1
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) 4) 00 cO00eo Co 1
-$4) 4) Qri 0 C'J ('J
oo Co Q$ 14) Park α Co ro O (%
J ('J ()001 Q4) 4 to 4) 4% 4% 4h → 4) 4$ (N ζq
guJ (N (', I N ('J pregnancy 4)
4) e'J CN CN ('J
ζJ
C Ku Department 04) La) O mouth epco 4% 4) CI co QO60Co a
ri eagle; U94) 4) 00 ako 0
o Co Cfri Q n4) 46 CIf 0 (%J ('J oo cO# 46
Hai 4) αfu 00 cy c'q CI
C1 ri + 4) - to N car
6 → To → E → to (A) Go to the bad lie button 4 To haisun pongu (Tsu → 【→ f4 & 4) We meet Sari size σ straw Tsuu E → To size hit C To dumb size Sohai u tou E 41 size O toku size bad pregnancy 4) 4) 4$4% 4 → To 4 → Toutt
UtoratoUtoUouu)U [→GuutoraE→To4)4)4
) 4 4t 4) 4) Uto → Toutou [Utou E t Uto → ÷ 4 wo Uouto → Shi4) 4) 4f 4) 4Th
4$ 4) → Sea lion →) Uto→) UtomisotoUto→E 昿トうE→ト昿)-N soso('4('J→ト→
Touto Pregnancy-heheN evil NN Uto → [hecv) evil size■N
CU 4%ken → To → wo sosogu sohe ('J4) → Togutsu\rdaitμ1nCQCtsu 4 4 →F →)
One C! 4) 414
) 4) 4) 4) 4) 4)...4) 4)
4) 4) 4+ 4) 4) 4) 4% 464)
4) 4G4) 4) 4) 41 UEu) 4%
→ )
Pregnancy 4) Department 4) Department 4% 4$ 4)
4) → To pregnancy fee → - Uto → To → To → To → To + to Ihehe Gingin to worship U8 Pregnancy 4% + fNC'J (A) Yen N 4
$ 4) ('tJ sokyousunsoheN pregnant →
Tosu ε 橿) Good beak (ShnCri I〉 4ヲ 45 sm dumb rot puss riu (→ 6 suri o toku suzōkenpō seishi 4) 4
$46 Pregnancy Pregnancy) To) Touo → [Utohai]
Toto Hai 4 To 4) Hai 4 To 4 wo To 4
Pregnancy (pregnancy) 4) U) 4) Uto → wo 訃kenhaiken pregnancy 4)
+ 4) 4) Pregnancy
) Utoidae → To 4) - Hehe Soso hehe → t Uto worship - to N Soso N pregnant → Tohe sori Toso to N → Ru Uto worship Tohe evil Suso NN class 4 *CQq Susun “P-Sudging Pregnancy”
Pregnancy department is over \1 lie 10! tgu】0wa4) 4) ho
d 仝3 Toro size Old To 1 Todo ω♀♀ Toro size High 4）Haiゝno=zooToゞHai →) 4$4←ゝ■Work；COC'
-Sa Giant family 4) 4) 4$ 4) 4!4) 4)
414) 4) 4) 4) 4) 4) 4)
4) 4 days) 4) 41414) 4) 4)
48 → 4 4) 4) 4! 4) 4゜0.88゜,
, Q Pregnancy 4) 4) 4) 464) 41 迿と昿E 連 と橿 1 迿 [语→ Leading) う) → を に が つ つ す - IN ('
Move E→H to 4F 1 inch σ)+N(N→
0 sun 4) 4) 4) 4) (
”-To ■’s height → E eaves 4 4) 4) 4) Pregnancy 4) 4)
4) 1 Pregnant → to Pak Kung to frog to → to → E (
Todomiro 4) 4) 4 4) 4 $ 4) Froging sea lion) Uto 4 → To ko To 噌 3) Todomiro 4) 4 To 4) 4 $ 4) 4) 4 $ 4 $
4) 4t 4) 4) 4) Tan 4t→)4t→
Tobakhai→) 4$ 414÷-hehekai-heNut→)4
) → を－HeN(I)M(%J('J→To frog size black slap size □□□4) 46 pregnancy 1 sun's ■ Toku size bad pregnancy > 00 ■ Irisunto pregnancy →) Sea lion Qη punishment short worship →E
Disability 4+4) 4) 4) 4) Department →) 4ト欿) Uto
4 to get pregnant
To) To → Sea lion) Uwo-he to evil N to J Giant trigram 4) Sea lion 00ω ri 1 sun) E Pregnancy worship Tototo Qω Kukugu worship 4 wo worship 4 Totodo) 4) 4) 4) →) Pregnancy → Toto misery → b→ to ÷ 6 to worship 4) 4) 4$ 4) Worship 4) 4 to pregnancy 4ε→) Ekahai to despise rumors →) Worship 4) Pregnancy still 4 to hai 4 to 4 to eken 訃shito pregnancy light decomposition u) Park → ku → t → shihai-NN (a) evil hehe 4$
4) → Steller's sea lion -hehe so G to N4t4) Slap C tokusunfang eaves kasumi C tokusunkai 4to4tototomeell)slapsandsunpregnant trigram 4B'-J-■ωsho C Lie Figure 11 Figure 12 ■■■■■■■■■■ Figure 13 Figure 15 (a) 4, 4 4 4 4 2 Estimated intermediate lesson (b) Bll B12 B12 B12 B128Bll B
12823 C21B23 B; B21 B22823
C21B23 B:A21 A12 A12 C2J
B12 BA21 Bll B12 C21Dll
DA21 Bll B12 B23 C21D:Dll
012012 C12C31D: C21C22C23
C23C23D: C31C32C32C32C33D: C41C42C43C43C43Dz Scanning aperture diagram 1111 row image, 2 Dll C12] M3014 23 C21C22C23D24 Seven 4 C31C32C33C34 -3C41C12C43C14 ,2C12C22C23C24 22C23C23C23C24 32C32C23C23C24 31C32C23C23C24 31C32C32C33C34 11C42C43C43D44 Selection status Figure 16 Figure 17 Diagram Input 16 c""+cqI:v)■ ■ ■CD ■ ■ ■ Cω of ■ ■ ■ ■ ■ ■ ■ of 0 ■
■ ■ ■ ■ ■ ■←←-←-+ + -+ m -1-lJ:l ('l') -1-F-1tlm-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-1-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-11-1-1-1 ----- ■■■■口口■■口■ 22nd 46 21623-10 Line 9-5 888-41
．． 335 (a) 2 826-24 734 4 44-6-2 1. 22 4.4460222 Bypass halftone image 4, 4 4 4 2 2 1 1 (b) 87654333 4 4, 4 4 3 2 2 24,
4 4 3 3 2 2 20-Bass halftone image 21 diagram 08210 Bypass dither image 0-Pass dither image 22 diagram column → 4. 4- 4 4 422111114 4.4
4 64411111 (a) 12101010
844333334, 4 4 4 44433222 4, 4 4 4 44222222 Enlarged halftone image 44.422111 44.642111 81.2888313 (B) 1210 844333 (C) 4
4 4.42222 4 4, 4.4-2222 Reduced halftone image Figure 23 Enlarged dither image Reduced dither image (a) 4 4, 4. 6 4. 2 1 1 1 1 line 4
4 4 6 4, 4 1. 1 1
18], 216888 1 3 1 31
2101.08443333 4 4 4, 4 4 4 3 2 2
24 4, 4. 4 4 2 2 2 2
24 4 4, 4 4 2 2
2 2 24, 4 4. 4 4 2
2 2 2 2 Estimated halftone image Figure 24 ()X) Dithered image

Claims (7)

[Claims] (1) In a dithered image created using a dither matrix, set multiple types of scanning apertures for each type, and find the only scanning aperture that satisfies predetermined conditions for each pixel of the halftone image to be estimated. A halftone image estimation method for a binary image, characterized in that a halftone image is estimated based on the number of white pixels or the number of black pixels within the selected scanning aperture. (2) The predetermined conditions mentioned above are conditions such that gradation is expressed using a large scanning aperture in the low spatial frequency region, and gradation is expressed using a small scanning aperture in the high spatial frequency region. A method for estimating a halftone image of a binary image according to claim 1. (3) The above-mentioned predetermined condition means that the dithered image within the scanning aperture and the halftone image created based on the number of white pixels or the number of black pixels within the scanning aperture are binarized using the dither matrix. Claim 1 is based on a matching condition of the patterns of these two binary images obtained by comparing for each aperture.
2. The method for estimating a halftone image of a binary image as described in 1. and 2. (4) A method for estimating a halftone image of a binary image according to claims 1 to 3, wherein the binary dithered image is a systematic binary dithered image. (5) The method for estimating a halftone image of a binary image according to claim 4, wherein the systematic binary dither image is a dot-dispersed binary dither image. (6) Claim 5, wherein the dot-dispersed binary dither image is a Bayer binary dither image.
The method for estimating a halftone image of a binary image as described in . (7) A patent characterized in that the size and shape of the scanning aperture with the largest area among the plurality of types of scanning apertures are equal to the size and shape of the threshold matrix of the systematic binary dithered image. A method for estimating a halftone image of a binary image according to claims 4 to 6.