JPS62157468A - Expanding/reducing method for binary image - Google Patents

Abstract

PURPOSE:To obtain an image close to an original half tone image by setting plural types of a scanning aperture, scanning on a dither image, counting the number of the white picture element in an equivalent scanning aperture and obtaining a presumed half tone image value. CONSTITUTION:Algorithm is used where in a low space frequency area (area where the picture element level change of a half tone image is small), a large aperture is selected and in a high space frequency area (area where the picture element change of the half tone image is large), a small aperture is selected, a scanning aperture D is a reference aperture first, processing is executed in the sequence of D C B A, and a suitable scanning aperture is selected. When the scanning aperture is not selected, the suitable aperture is selected in the route of D E G and D F G. Next, based on the ratio of the white area and the black area in the scanning aperture, a half tone image is presumed.

Description

[Detailed description of the invention] (Industrial application field) The present invention relates to a method for enlarging/reducing a binary image.

(Prior Art) As a method for enlarging/reducing a 2ft1 image, the following methods are conventionally known.

(2) SPC method Selects one original pixel closest to the converted pixel and uses its density value as the converted pixel value.

■Logical sum method exchange pixel density) r to the nearest four original pixels A and B.

Output as the logical sum of the concentrations Ia, Ib, Ic, and Id of C and D. In other words, ! r-1aLIIbUIcUId (1)■
9-division method Divide the rectangular area whose vertices are the original pixel positions A, B, C, and D into nine partial areas 01 to Gs, and divide the rectangular area into nine partial areas Gl (1-1 to 9) containing the converted pixels. The converted pixel density (r) is determined using a predetermined logical expression determined according to the following equation.For example, in the case of ll1-8, lr is determined by the following equation.

Ir-1d Ula (2>■
The average density of the original pixel projected onto the projection method converted pixel is determined, and that value is subjected to threshold processing to obtain the converted pixel value.

(Problems to be Solved by the Invention) In the case of the SPC method, although the processing is simple, there is a problem that the strokes tend to become thinner during reduction, and the connectivity of the strokes is likely to be lost, making it difficult to handle. In the case of the logical sum method, the strokes become thicker, but there is a problem in that unnecessary connections of strokes occur during reduction, resulting in conspicuous distortion of the image. In the case of the 9-division method, it is possible to improve the visibility of the image by making the strokes thicker when reducing the image, but the strokes also become thicker when the image is enlarged, resulting in a problem of deterioration of the image quality. Finally, in the case of the projection method, the transformed image is less likely to be missing or distorted, and the similarity of the original image is well preserved. However, the calculation process L! [! Since there are many m, there is a problem that the processing time becomes long.

By the way, when a pseudo intermediate I12 value image such as a dithered image is enlarged or reduced using conventional techniques, there are disadvantageous phenomena such as occurrence of moire fringes, reduction in resolution, and change in gradation pattern. Furthermore, when enlarging a binary image such as a character or a line drawing, there is an inconvenient phenomenon in which the characters become blurred and when reduced, thin lines become blurred, resulting in significant image deterioration.

The present invention has been made in view of these points, and its purpose is to improve 211-value images such as characters and line drawings that are essentially only black or white, and dithered images that express halftones in a pseudo manner. The object of the present invention is to realize a method for enlarging/reducing a binary image by which both can be enlarged/reduced satisfactorily. The present invention is very advantageous in enlarging and reducing binary images containing a mixture of characters and halftone images.

(Means for Solving the Problems) The present invention solves the above-mentioned problems by adding the intermediate 11Wi to be estimated on a binary image created by a dither matrix.
A plurality of types of scanning apertures, including one having a size of at least 1 pixel x 1 pixel, are set and moved for each pixel of the image, and a scanning aperture that satisfies a predetermined condition is selected from among the plurality of types of scanning apertures by the dithering. When selecting a unique scanning aperture while moving pixel by pixel in the image and estimating a halftone image based on the ratio of white areas to black areas within the scanning aperture, the dithered image within each scanning aperture and the A halftone image created based on the ratio of the white area to the black area within the scanning aperture is compared with a binary image obtained by the dither matrix within the scanning aperture for each scanning aperture, and a unique scanning aperture is selected. A halftone image is estimated by using a first selection method that selects a unique scanning aperture by performing predetermined calculation processing based on the white area and black area within each scanning aperture. The method is characterized in that the estimated halftone image is enlarged or reduced.

(Function) The present invention sets a plurality of types of scanning apertures including those having a size of at least 1 pixel x 1 pixel for each pixel, performs a predetermined judgment process, and selects one scanning aperture for each pixel. I decided to do so. As a sequence, the present invention returns a binary image to a halftone image, enlarges/reduces the halftone image,
The image is then binarized again to create an enlarged binary image. When returning a binary image to halftone, images that are essentially halftone, such as dithered images, are estimated to be as close to the original halftone image as possible, and text and line drawings are estimated to be as close to the original white as possible. A method of estimating while keeping the color black is adopted.

(Example) Hereinafter, an example of the present invention will be described in detail with reference to the drawings. Here, we first introduce 8×8 as one of the systematic dither methods.
An example will be explained in which a Bayer type matrix of 1 is used as a threshold matrix.

FIG. 1 is a flowchart showing one embodiment of the present invention. The following will explain along this flowchart.

(1) Step (2) A plurality of types of scanning apertures including one having a size of at least 1 pixel x 1 pixel are set for each pixel in a binary image consisting of a white area and a black area.

FIG. 2 is a diagram showing an example of a matrix for explaining the present invention. (A) is the original halftone image converted to digital data, (B) is an 8x8 Bayer type dithered value matrix, and (C) is converted to a black and white binary image (dithered image) using a threshold matrix (B). This is a dithered image (binary II image) of the original image (A). Here, the Bayer threshold matrix has a dither pattern in which dots are dispersed as shown in Figure (b).

FIG. 3 is a diagram showing an example of multiple types of scanning apertures used in the present invention. Z has a size of 1 row x 1 row, A has a size of 2 rows x 2 columns, B has a size of 2 rows x 4 columns, and C has a size of 4 rows x 2.
The column size, D is 4 rows x 4 columns, and E is 4 rows x 4 columns.
The scanning aperture has a size of 8 columns, F has a size of 8 rows x 4 columns, and G has a size of 8 rows x 8 columns. As is clear from the figure, the scanning apertures of 2 rows by 1 column and 1 row by 2 columns are not used because they have poor image restoration characteristics for images in which character images and gradation images are mixed. Here, the black circles shown in each of the scanning apertures Z, A to G are the centers of movement when moving on the dithered image in FIG. 2(c).

Note that both rows and columns correspond to pixels. For example, the size of 2 rows x 4 columns corresponds to the size of 2 pixels x 4 pixels. The reason why 1 pixel x 1 pixel was selected as the scanning aperture is to reproduce the edges of character images well from an image in which character images and gradation images coexist.

Incidentally, while keeping the scanning apertures shown in FIG. 3 fixed, we move them on the dithered image in FIG. If the estimated values of the half-tone image are counted, the estimated middle-tone images as shown in FIGS. 4(A) to 4(H) are obtained. Here, (A) is according to Figure 3 2, (B) is according to Figure 3, (C) is according to Figure 3 B, (D) is according to Figure 3 C, and (E) is according to Figure 3. , (F) are the images from FIG. 3E, (G) are from FIG. 3F, and (H) are the middle+i+a images from FIG. 3G, respectively.

Here, first, a method for obtaining the halftone image shown in FIG. 4(H) will be explained.

Now, move the scanning aperture G defined in FIG. 3 to the initial position of the dithered image as shown in FIG. Confucianism.
The dithered image is omitted here. In this case, it is desirable that each pixel contained within the scanning aperture be completely contained, as shown in the figure. That is, it is desirable to prevent a certain pixel from being partially missing. Note that the black value is shown with diagonal lines here to make it easier to see.

Next, the number of white pixels in the area surrounded by the scanning aperture G is counted, and this value is used as the estimated value of the halftone. The number of white pixels within the scanning width G in the state shown in the figure is 21. Therefore, the estimated value of the first row and first column (1, 1>) of the estimated halftone image is 21.Next, the scanning aperture G is moved by one pixel (one column in this case), and the scanning aperture G If the number of white pixels within is counted in the same manner as described above, it will be 20. The same operation is performed for the same.

When the first row is completed, move the scanning aperture G by one row to the next row and start the intermediate density estimation operation from the bottom right of the pixel whose center is (5, 4>. The halftone image estimation operation is completed by moving the scanning aperture to the last column of the row to obtain the halftone image estimation value, and the halftone image shown in FIG. 4(H) is obtained.

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

In this case, since it is necessary to align the center of movement with the largest scanning aperture G, the movement start position of the scanning aperture will be as shown in FIG. The number of white pixels in this state is 3
In order to match the area to Figure 3G, it is necessary to quadruple the number of white pixels in the aperture, so the number of white pixels is 3.
It becomes x4-12. In this case, the gain of the scanning aperture is 4
It is said that Similarly, find the gain of each scanning aperture in Fig. 3, Z Gt 64, A C 4, B 8, C
Lt2, E is 2, F is 2, and G is 1. If such a calculation is performed every time the scanning aperture is moved by one pixel, the estimated halftone image shown in FIG. 4(e) is obtained. Figure 4 (a)
~(d), kube), and (g) can be considered in the same way, so their explanations will be omitted.

A halftone image can also be estimated relatively well by the method described above. The data in FIG. 4 is a diagram showing the estimated halftone image obtained in this manner. Of course, with this method, the dithered image (see Fig. 2(c)) with fewer information groups than the original halftone image shown in Fig. 2(a)
Since the halftone image is estimated from the original halftone image, the original halftone image is not completely restored as shown in FIG. However, except where the pixel level of the original halftone image changes rapidly, a halftone image that is fairly close to the original halftone image is obtained.

In particular, when there is no change in the pixel level of the original halftone image within the scanning aperture G, the estimated halftone pixel level perfectly matches the original halftone il1 image value.

By the way, human vision has a high gradation discrimination ability in the low spatial frequency region (region where there are few changes in the pixel level of the halftone image), and has a high ability to discriminate gradations in the high spatial frequency region (the region where there are many changes in the pixel level of the halftone image). It has a characteristic of having low pixel level gradation discrimination ability. Therefore, if high gradation is expressed using a large scanning aperture in the low spatial frequency region, and a high resolution image is reproduced using a small scanning aperture in the high spatial frequency region, each scanning aperture shown in Figure 4 It is possible to estimate a halftone image better than a single halftone image estimation value by .

(2) Step ■ A dithered image within a specific scanning aperture and a halftone image created based on the ratio of white areas and black areas within the scanning aperture are combined with a binary image obtained by the dithering matrix within the scanning aperture. are compared in a predetermined order of scanning apertures to select a unique scanning aperture.

Next, this comparison method will be explained. This method assumes that digital binary images have already been stored in storage means such as a memory, sets multiple types of scanning apertures for these digital binary images, and performs predetermined arithmetic processing on the digital binary images. Then, for each pixel, one of the plurality of types of scanning apertures is selected, and the number of white pixels (or the number of black pixels) within the selected scanning aperture is counted to estimate a halftone image. It is something that obtains value. The predetermined arithmetic processing includes a large opening in the low spatial frequency region (region where there are few changes in the pixel level of the halftone image) and a small opening in the high spatial frequency region blt (region where there are many changes in the pixel level of the halftone image). An algorithm is used as selected.

The basic idea of the invention is to select a scan aperture that is as large as possible, as long as no density changes are observed within the scan aperture. FIG. 7 shows the selection order of scanning apertures in the method of the invention. Using the scanning aperture as a reference aperture, first, the process is performed in the order of D-4C-4B→A, and the optimum scanning aperture is selected using the first method described later. If no scanning aperture is selected by the first method, then the second method is used to select an optimal aperture along the route D→E−+G or D→F→G.

Step (1) Here, first, D is considered as the scanning aperture. Then, when the selected width is superimposed on the position (5°6) of Fig. 2 (c), that is, on (2, 3>) of the designated halftone image, it becomes as shown in Fig. 8 (a).
It will be as shown in. The number of white pixels within this aperture is counted as 6. This number of white pixels is 6 and the gain is 4, which is 24.
Assuming that is the average pixel level, each pixel is compensated by 24 as shown in (b). The average pixel level image shown in (b) is shown in (c) ll1lil! 2 in matrix
When converted into a value, it becomes something like the one shown in (d).

Here, when comparing the dithered image (a), which is the original binary image, and the re-binary image (d), they are not the same pattern. In other words,
There is a mismatch. The fact that the patterns (a) and (d) are not the same pattern means that there was a change in the pixel level of the original halftone image within the aperture. Therefore, in this case as well, the scanning aperture is inappropriate. Since no scanning aperture was selected in step (1), step (2)
). If the patterns (a) and (d) are the same pattern, it means that no change in the pixel level of the halftone image is detected. Therefore, the size of the scanning aperture is greater than or equal to 0, and the process proceeds to step (2).

Process (2) The scanning aperture selected in step (2) is C.

Then, when the selection aperture C is superimposed on the initial position of FIG. 2 (c), the result is as shown in (e). The number of white pixels within this scanning aperture is counted as 2. Assuming that 16, which is the number of white pixels multiplied by a gain of 8, is the average pixel level (to)
Each pixel is padded with 16 as shown in . The average pixel level image shown in (f) is converted into a binary value by the threshold matrix shown in (g) (consisting of the threshold matrix in Fig. 2 (b) and the second and third columns, that is, the threshold matrix within the scanning aperture). to become
The result will be as shown in (H).

Here, when the dithered image (E), which is the original binary image, and the re-binary image (H) are compared, they have the same pattern. That is, they match. The fact that the patterns (E) and (H) are the same pattern means that there is no change in pixel level. Therefore, in this case, the scanning aperture C is appropriate.

If they do not match, the process of considering the next scanning aperture B (
Proceed to 3).

Step (3) In step (3), the same process as in steps (1) and (2) is performed for frontage B, but this process is not necessary in this example.

Note that even if they do not match to the end, the smallest scanning aperture 2 is selected.

When the scanning aperture C is selected in this manner, the number of white pixels within the scanning aperture C is 2 as described above. Scanning amount (
Since the gain of ic is 8, the estimated image value to be obtained is 2×8−16. That is, the pixel level shown in (f) directly serves as the image estimate value.

<3) Step ■ Check whether a scanning aperture that satisfies the conditions has been determined in step ■.

As explained in step (1) of step (2), if the original binary image in FIG. 8(a) and the re-binary image in FIG. Therefore, if a scanning aperture smaller than this is selected, the size of the frontage will be indicated, and the gradation will deteriorate, so the aperture selection process ends here.

Therefore, in this case, the first method described in step (2) cannot be used, and the following second method must be used.

(4) Step ■ A unique scanning aperture is selected by performing predetermined arithmetic processing based on the white area and black area within the plurality of scanning apertures.

First, as a scanning aperture, one having the size of DG shown in FIG. 3 will be used. The number of white pixels in each scanning aperture is then set to d-9.

Then, the condition that there is no change in pixel level is determined as follows.

12d-el≦1 (1) 12d
-fl≦1 (2) 12e-g
l:1 (3) 12f-g l
≦1 (4) The scanning aperture to be used according to each condition is determined as shown in FIG. 9, with the case where each of these conditions is satisfied as 0 and the case where it is not satisfied as X. Here, the * mark in the figure indicates O or ×.

For example, if formulas (1) and <2> are not satisfied, (
3), an aperture is selected without checking whether it satisfies formulas (4), and if formula (1) is satisfied but formula (2) is not satisfied, the aperture E is If the equation (1) is not satisfied but the equation (2) is satisfied, the aperture F is selected. If (1) to (4) the price is satisfied, opening G is selected.

Under the above conditions, the optimum scanning aperture will be determined when the center position of each scanning aperture in the dithered image shown in FIG. 2(C) is at the lower right of the <4.4) pixel. In this case, d-3, e-9, r
=8. It will be Q-21. First, conditional expressions (1) and (2)
Let's try to find the formula.

12d -e I = 16-91 = 3, equation (1) is not satisfied, l 2d -e 1 = 16-81-2, equation (2) is not satisfied, therefore, the optimal aperture is found according to Fig. 9, and D becomes. The first halftone image when D is selected as the scanning aperture.
Estimate the value for the pixel in the first row and column. When the scanning aperture is selected, the number of white pixels at the initial position is d-3, and the gain of the scanning aperture is 4, so the halftone image estimated value is 3x4=
It becomes 12.

Through the above operations, one optimal scanning aperture is selected for each pixel. FIG. 10 is a diagram showing the sequence of scanning aperture selection, and summarizes the explanation so far.

(5) Step■ A halftone image is estimated based on the determined scanning aperture.

By the first method or the second method described above, one optimal scanning aperture is always found for one pixel.

Therefore, a halftone image can be estimated based on the ratio of white areas to black areas within the scanning aperture.

For example, it is conceivable to use the number of white pixels within the scanning aperture as the estimated value.

FIG. 11 is a diagram showing an estimated halftone image obtained in this manner. Incidentally, to explain which scanning aperture was used for each halftone image estimation, taking the case of the first row as an example, <i, i> of the halftone estimation image is D, (1, 2> is D, ( 1
, 3> is C, (1°4) is B, <1.5) is C, (1,
6) is 81 (1,7) is B, (1,8> is C, (1,9
> is C. FIG. 11(b) is a diagram showing examples of all selected frontages.

The estimated halftone image, which is not shown in FIG. Since the tonal image is estimated, it is in line with human visual characteristics. Therefore, the estimated halftone image is extremely close to the original halftone image shown in FIG. 2(a).

(6) Step■ Enlargement/reduction processing is performed on the estimated halftone image.

Next, the estimated halftone image shown in FIG. 11 is enlarged/reduced,
It is also possible to create a new binary image by using a threshold matrix for the enlarged/reduced halftone image. The flowchart at this time is as shown in FIG. expansion·
As a reduction method, for example, an interpolation method is used. 1st
FIG. 3(a) shows the halftone image shown in FIG. 11(a) using the nearest neighbor method.
(b) is a halftone image enlarged by a factor of 1.25 using the ighborhood method), and (b) is a halftone image reduced by a factor of <0.75.

(7) When these halftone images in step are binarized using the dither matrices shown in (c) and (d), (e),
A binary image as shown in (f) is obtained.

In both of the above-mentioned embodiments, when estimating a halftone image from a binary image, the binary image is preferably a dithered image or a density pattern image, and particularly preferably a dithered image. The dithered image is preferably a dithered image using a systematic dithering method so that one threshold value is applied to each scanning aperture with the largest area, rather than random dithering or conditional dithering, and the threshold value is evenly applied to each scanning aperture with the smallest area. A distributed dithered image with a completely distributed threshold value is preferred, and a Bayer dithered image with a completely distributed threshold value is particularly preferred.

It is preferable that the size of the scanning aperture with the largest area among the plurality of types of scanning apertures is equal to the size of the threshold value matrix of the systematic dither image. Specifically, Digital 2
Assuming that the value images are already stored in a storage means such as a memory, multiple types of scanning apertures are set for these digital binary images, and predetermined arithmetic processing is performed on the digital binary images so that one pixel is For each scanning aperture, one of the plurality of types of scanning apertures is selected, and the number of white pixels (or the number of black pixels) within the selected scanning aperture is counted to obtain an estimated value of the intermediate aperture image. It is. The predetermined calculation process is as follows:
The method of the first embodiment and the method of the second embodiment are used.

In the above description, the case where the number of white pixels within the scanning aperture is counted to estimate a halftone image has been taken as an example.

However, the present invention is not limited to this, and any method may be used as long as it estimates a halftone image based on the ratio of white areas to black areas within the scanning aperture. In the above description, halftones are 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. Further, in the above description, the case where there are four types of scanning apertures is taken as an example, but the present invention is not limited to this, and any type may be used. Furthermore, the size of the scanning aperture does not need to be limited to the illustrated one, and any size can be used.

(Effects of the Invention) As described in detail above, according to the present invention, a plurality of types of scanning apertures are set, and dithering is performed while selecting an optimal scanning aperture for each pixel through predetermined processing. By scanning the image, counting the number of white pixels within the scanning aperture, and using the counted value as the estimated halftone image value, an image close to the original halftone image can be obtained. The halftone image estimated value obtained in this way takes into account the human congratulatory characteristics, so the original halftone image
It becomes closer to one image. Once the halftone image is obtained, various processes such as enlarging and reducing the halftone image can be performed on the halftone image. According to the present invention, it is possible to satisfactorily enlarge or reduce a binary image containing a mixture of text/line drawings and halftone images, which has a great practical effect.

[Brief explanation of drawings]

FIG. 1 is a flowchart showing an embodiment of the method of the present invention;
Fig. 2 is an explanatory diagram when obtaining a dithered image from an original halftone image, Fig. 3 is a diagram showing multiple types of scanning apertures, and Fig. 4
The figure shows an example of the @tone image during estimation of each aperture obtained.
6 is a diagram showing the initial position of the aperture, FIG. 7 is a diagram showing the order of aperture selection, FIGS. 8 to 10 are explanatory diagrams of the method of the present invention, and FIG. 11 is a halftone image obtained. 12 is a flowchart showing a method of enlarging/reducing a halftone image, and FIG. 13 is a diagram showing a binarization process by enlarging/reducing. Patent Applicant Roku Konishi Photo Industry Co., Ltd. Agent Patent Attorney Fuji Ijima 1 person (c) Di-Song Figure 3, Figure 7, Figure 5 Figure 6, Figure 7, Figure 4 (A) Opening 2 (B) Opening A (E) F
M opening D (f) Opening E diagram (c) Opening B (d) IIIDC (
g) Sekiguchi F (ch) a-guchi G light 11 diagram (a) (b)

Claims (8)

[Claims] (1) On the binary image created by the dither matrix,
At least 1 pixel × for each pixel of the halftone image to be estimated
A plurality of types of scanning apertures, including those having a size of one pixel, are set and moved, and among these plurality of types of scanning apertures, a scanning aperture that satisfies a predetermined condition is selected while moving pixel by pixel of the dithered image. When selecting a scanning aperture and estimating a halftone image based on the ratio of the white area to the black area within the scanning aperture, the dithered image within each scanning aperture and the ratio of the white area to the black area within the scanning aperture are estimated. A halftone image created based on the ratio is obtained by a dither matrix within the scanning aperture.
A first selection method involves comparing the value images for each scanning aperture to select a unique scanning aperture, and performing predetermined arithmetic processing based on the white area and black area within each scanning aperture. A method for enlarging/reducing a binary image, characterized in that a second selection method for selecting a scanning aperture is used in combination to estimate a halftone image, and the estimated halftone image is enlarged/reduced. (2) The plurality of types of scanning apertures include 2 pixels x 1 pixel and 1 pixel.
2. The method for enlarging/reducing a binary image according to claim 1, wherein the method does not include an image having a size of pixel×2 pixels. (3) The method for enlarging/reducing a binary image according to claim 1, wherein the binary image includes a pseudo-halftone binary image. (4) The method for enlarging/reducing a binary image according to claim 3, wherein the pseudo halftone binary image is a dithered image. (5) The method for enlarging/reducing a binary image according to claim 4, wherein the dithered image is a systematic dithered image. (6) The method for enlarging/reducing a binary image according to claim 4, wherein the dithered image is a dot-dispersed dithered image. (7) The method for enlarging/reducing a binary image according to claim 6, wherein the dot-dispersed dithered image is a Bayer dithered image. (8) The size and shape of the scanning aperture with the largest area among the plurality of types of scanning apertures are made to be equal in size and shape to the threshold matrix of the systematic dither image. A method for enlarging/reducing a binary image according to items 5 to 7.