JPH047149B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a method for enlarging/reducing a binary image.

(Prior Art) The following methods are conventionally known as methods for enlarging and reducing binary images.

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

Logical sum method The exchange pixel density Ir is the nearest 4 original pixels A, B,
Output as the logical sum of the concentrations Ia, Ib, Ic, and Id of C and D. That is, Ir=IaUIbUIcUId (1) 9-division method Divide the rectangular area whose vertices are the positions of the original pixel, A, B, C, and D into 9 partial areas G ₁ to G ₉ that include the converted pixels. The converted pixel density Ir is determined using a predetermined logical operation formula determined according to the partial region Gm (m=1 to 9). For example, when m=8, Ir can be found by the following formula.

Ir=IdUIa (2) Projection method A method that calculates the average density fr of the original pixel projected onto the converted pixel and applies threshold processing to that value to obtain the converted pixel value Ir.

(Problems to be Solved by the Invention) In the case of the SPC method, the processing is simple, but there is a problem that strokes tend to become thinner during reduction, and omissions are likely to occur due to loss of stroke connectivity. 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, making the collapse of the image noticeable. In the case of the 9-division method, it is possible to improve the visibility of the image by thickening the strokes when reducing the image, but the problem is that the strokes also become thicker when the image is enlarged, which deteriorates the image quality. be. 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, since the amount of calculation processing is large, there is a problem that the processing time becomes long.

By the way, when a pseudo-halftone binary image such as a dither image is enlarged or reduced using the conventional technique, disadvantageous phenomena such as the occurrence of moiré fringes, a decrease in resolution, and a change in gradation pattern occur. Furthermore, when enlarging a binary image such as a character or a line drawing, there is an inconvenient phenomenon in which the characters are crushed, 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 binary 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 using pixels on a binary image consisting of a white area and a black area, which correspond to each pixel of a halftone image to be estimated. to the pixel that
including a scanning aperture with a size of 1 pixel by 1 pixel and 2
The first step is to set a plurality of types of scanning apertures that do not include scanning apertures with sizes of pixel x 1 pixel and 1 pixel x 2 pixels.
selecting said scanning apertures in an order such that the size of the scanning apertures decreases, and counting the number of white or black pixels in the image portion enclosed by this selected scanning aperture in said binary image; This counted number of pixels is multiplied by the reciprocal of the area ratio of the selected scanning aperture to the scanning aperture of the maximum size, and the obtained value is used as a tentative halftone estimate of the pixel in the halftone image. a second step of re-binarizing the halftone estimated value of the pixel of the halftone image to be estimated by using a dither matrix having the same size as the scanning aperture; The re-binary image obtained in the second step is compared with the image portion of the original binary image surrounded by the selected scanning aperture, and when they are first equal, the selected scanning aperture is The provisional halftone estimate is set as the official halftone estimate at the pixel of the halftone image to be estimated, and the official halftone estimate obtained for all pixels of the halftone image to be estimated is used. The third step is to create a halftone image using
a fourth step in which the halftone image obtained in the third step is subjected to enlarging/reducing processing, and then binarized using a dither matrix to obtain a binary image; It is characterized by having the following.

(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 determination process, and selects one scanning aperture for each pixel. I decided to do so. As a sequence, the present invention once returns a binary image to a halftone image, enlarges or reduces the halftone image, and binarizes it 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 keeping it black is adopted.

(Example) Hereinafter, an example of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a flowchart showing one embodiment of the present invention. This flowchart will be explained below.

Step: A plurality of types of 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 region and a black region.

Here, we will first introduce 4 methods as one of the systematic dither methods.
An example will be explained in which a ×4 Bayer matrix is used as a threshold matrix.

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 the 4×4 Bayer dither threshold matrix, and C is the dither of the original image A that has been converted to a black and white binary image (dither image) by the threshold matrix S. It is an image (binary image). Here, the Bayer threshold matrix has a dither pattern in which dots are dispersed, as shown in FIG.

FIG. 3 is a diagram showing an example of multiple types of scanning apertures (unit areas) used in the present invention. Z has a size of 1 row x 1 column, A has a size of 2 rows x 2 columns, and B has a size of 2
C indicates a scanning aperture having a size of 4 rows x 2 columns, and D indicates a scanning aperture having a size of 4 rows x 4 columns. In this embodiment, as is clear from the figure, 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 D are the movement centers when moving on the dithered image in FIG. 2C. 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×1 pixel was selected as the scanning aperture is to reproduce character images well from an image in which character images and gradation images coexist.
The present invention selects one optimal aperture from among these multiple types of apertures, but in selecting the most optimal aperture, the following needs to be considered. In other words, human vision has a high gradation discrimination ability in low spatial frequency regions (regions with few pixel level changes), but only a low gradation discrimination ability in high spatial frequency regions (regions with many pixel level changes). It has the characteristic that there is no

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, a high-quality halftone image can be obtained as a whole. I can do it. Incidentally, while the scanning aperture shown in FIG. 3 is kept fixed, it is moved on the dithered image shown in FIG. When the estimated value of the halftone image is counted, estimated halftone images as shown in FIG. Here, A is according to Figure 3 Z,
B is a halftone image according to FIG. 3A, C is a halftone image according to FIG. 3B, D is a halftone image according to FIG. 3C, and E is a halftone image according to FIG. 3D.

step First, the maximum aperture D is selected.

As explained in step 1, the basic idea of the invention is to select an aperture that is as large as possible as long as no density changes of the original halftone image are observed within the aperture. Therefore, here, the order of aperture selection is D→C→B→A as shown in FIG.
→ Take it to Z. A method for obtaining the halftone image shown in FIG. 4E will be explained.

Now, move the scanning aperture defined in E to the initial position of the dithered image (the center of movement is at the second row, second
The position at the bottom right of the column. (hereinafter referred to as (2, 2))
Overlay on. 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 values are shown with diagonal lines here for ease of viewing.

Next, the number of white pixels in the area surrounded by this scanning aperture is counted, and this value is used as the estimated value of the halftone. In the state shown in the figure, the number of white pixels within the scanning aperture is 7. Therefore, the first row and first column (1,
The estimated value of 1) is 7. Next, the scanning aperture is moved by one pixel (in this case, one column), and the number of white pixels within the scanning aperture is counted in the same manner as described above, resulting in seven. Perform the same operation for accompanying. When the first row is completed, the scanning aperture D is moved to the next row by one row, and the intermediate density estimation operation is started from the lower right of the pixel whose center is (3, 2). Then, the scanning aperture is moved to the last column of the last row to obtain the halftone image estimated value, and the halftone image estimation operation is completed, and the halftone image shown in FIG. 4E is obtained.

Next, a method for obtaining an estimated halftone image using the scanning aperture B shown in FIG. 4C will be explained. In this case, since it is necessary to align the movement center with the largest scanning aperture D, the movement start position of the scanning aperture B will be as shown in FIG. The number of white pixels in this state is 2, and in order to match the area to D in FIG. 3, it is necessary to double the number of white pixels within the aperture, so the number of white pixels is 2×2=4. In this case, the gain of the scanning aperture B is said to be 2. Similarly, when the gains of each scanning aperture in FIG. 3 are determined, Z is 16, A is 4, and C is 2. If such a calculation is performed every time the scanning aperture B is moved by one pixel, the estimated halftone image shown in FIG. 4C is obtained.
Since it is sufficient to consider the points A, B, F, and D in FIG. 4 in the same way, their explanations will be omitted.

Halftone images can also be estimated relatively well using the method described above. The data in FIG. 4 is a diagram showing the estimated halftone image obtained in this manner. . Of course, in such a method, the second
Since the halftone image is estimated from the dithered image (FIG. 4C), which has a smaller amount of information than the original halftone image shown in FIG. do not have. 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 density change within the scanning aperture D, the estimated halftone image value completely matches the original halftone image value. Therefore, based on the principle described above, if a large scanning aperture is used in the low spatial frequency region to express high gradations, and a small aperture is used in the high spatial frequency region to reproduce an image with high resolution, as shown in Figure 4. It is possible to estimate a halftone image even better than the halftone image estimation value shown in FIG.

Step: Obtain an estimated value based on the ratio of the white area to the black area within the selected aperture, and re-binarize this estimated value using a dither matrix corresponding to the size of the aperture.

Here, assuming that digital binary 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. 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 obtain an estimated value of the halftone image. This is what you get. The predetermined arithmetic processing includes a large aperture in a low spatial frequency region (a region where there are few changes in pixel level),
High spatial frequency region (region with many pixel level changes)
An algorithm is used such that a small aperture is selected in .

This will be explained using FIG. Here, as described above, first, D is selected as the scanning aperture. Then, when the selection aperture D is superimposed on the initial position of FIG. 2C (see FIG. 6), the result is as shown in FIG. 8A. The number of white pixels within this aperture is counted as 7.
Assuming that this number of white pixels, 7, is the average pixel level, each pixel is filled in with 7 as shown in FIG.
When the average pixel level image shown in B is binarized using the threshold matrix shown in C, a binary image (re-binary image) as shown in D is obtained.

Step Check whether the original binary image and the re-binary image match.

To explain FIG. 8, the original binary image A and the re-binary image D shown in A will be compared. As is clear from the figure, patterns A and D do not match. The fact that the patterns A and D are not the same pattern means that a pixel level change has occurred. Therefore, in this case, the scanning aperture D is inappropriate. Since scanning aperture D was not selected in step (1), the process proceeds to step (1).

Step Select the next scan aperture. Here, the scanning aperture C is selected next in the scanning aperture selection order shown in FIG. Then repeat the steps. When the selection aperture C is superimposed on the initial position shown in FIG. 2C, the result is as shown in E. The number of white pixels within this scanning aperture is counted as four. Assuming that 8, which is the number of white pixels multiplied by a gain of 2, is the average pixel level, each pixel is filled in with 8 as shown in F. When the average pixel level image shown in F is binarized using the threshold matrix shown in G (consisting of the threshold matrix in B of Figure 2 and the second and third columns, that is, the threshold matrix within the scanning aperture), the image shown in H A binary image can be obtained. here,
Comparing the original binary image H and the re-binary image H, they are not the same pattern. In other words, there is a mismatch. The fact that the ho and chi patterns are not the same pattern means that
This means that there has been a pixel level change. Therefore, in this case as well, the scanning aperture C is inappropriate. Since scanning aperture C was not selected in step (2), step 2 is performed again.

The next selected scan aperture is B. and,
When the selection aperture B is superimposed on the initial position shown in FIG. 2C, the result is as shown in FIG. The number of white pixels within this aperture is counted as 2. Assuming that 4, which is obtained by multiplying the number of white pixels by a gain, is the average pixel level, each pixel is padded with 4 as shown in FIG. When the average pixel level image shown in ``E'' is binarized using the threshold value matrix shown in ``L'', a re-binary image shown in ``E'' is obtained.
Here, when comparing the original binary image RI and the re-binary image O, they have the same pattern. That is, both patterns match. The fact that patterns A and D are the same pattern means that there is no change in pixel level within this scanning aperture. Therefore, in this case, the scanning aperture B is appropriate. If they do not match, the same operation is repeated until the last scanning aperture (Z in this case). That is, the scan aperture Z is ultimately the scan aperture to be determined.

Step: Take the estimated value obtained from the matched scanning aperture as the halftone image estimated value of the pixel.

Once scanning aperture B is selected as described above,
The number of white pixels within the scanning aperture B is 2 as described above.
It is. Since the gain of the scanning aperture B is 2, the estimated image value to be obtained is 2×2=4. That is,
The pixel level shown in (b) directly serves as the image estimate value. When the above operations are performed for each pixel of the dithered image (binary image) shown in FIG. 2C, an estimated halftone image as shown in FIG. 9 is obtained. By the way,
Which scanning aperture was used for each halftone image estimation
Taking the case of the first row as an example, (1, 1) of the estimated halftone image is B, (1, 2) is B, (1,
3) is B, (1,4) is A, (1,5) is A, (1,
6) is B, and (1,7) is D. FIG. 10 is a diagram showing all selection apertures.

The estimated halftone image shown in Figure 9 uses a large scanning aperture to estimate a halftone image in areas where there are few changes in pixel level, and uses a small scanning aperture to estimate a halftone image in areas where there are many changes in pixel level. Therefore, it is in line with human visual characteristics. Therefore, the estimated halftone image is extremely close to the original halftone image shown in FIG. 2A.

Step Performs enlargement/reduction processing on the obtained halftone image,
The halftone image after enlargement/reduction is binarized again.

FIG. 11 is a flowchart showing the case of enlarging/reducing an estimated halftone image. The flowchart shown in the figure is for enlarging/reducing a halftone image estimated according to the present invention, and obtaining a new binary image using a threshold matrix for the enlarged/reduced halftone image. For example, an interpolation method is used as a method of enlarging/reducing.

Figure 12A shows the halftone image shown in Figure 9 in the Nearest Neighborhood.
(b) is a halftone image enlarged to 1.25 times, and B is a halftone image also reduced to 0.75 times. When these halftone images are re-binarized using the dither matrices shown in C and D, respectively,
A binary image as shown in F is obtained.

The dithered image is preferably a dithered image using a systematic dithering method so that one threshold value is applied to each aperture with the largest area than a random dither or conditional dithered image, and the threshold value is evenly applied to apertures with a small area. A distributed dithered image such as this is preferred, and a Bayer dithered image with completely distributed threshold values is particularly preferred.

FIG. 13 is a diagram showing an example of an apparatus for realizing the method of the present invention. In the figure, 1 is a memory in which a binary image consisting of a white area and a black area is stored;
3 is a memory in which a plurality of types of scanning apertures are stored, including a scanning aperture with a size of 1 pixel x 1 pixel and not including a scanning aperture with a size of 2 pixels x 1 pixel and 1 pixel x 2 pixels; A plurality of stored scanning apertures are selectively read out in order of decreasing size, and based on the selected scanning aperture, an image of the binary image in the memory 1 surrounded by the selected scanning aperture is read out. Central control means 4 counts the number of white or black pixels of the image data output by the central control means 3, and further calculates the area ratio of the selected scanning aperture to the maximum size scanning aperture. This counted pixel number is multiplied by the reciprocal of Binarize again using a dither matrix of the same size as
Re-binary image forming means 6 obtains a re-binary image for the relevant pixel of the halftone image, and 6 represents the re-binary image data obtained by the re-binary image forming means 4 and the central control means 3.
is a comparison means for comparing the original binary image output by the image data surrounded by the selected scanning aperture. When the central control means 3 receives a signal from the comparison means 6 that the two images are the same, the central control means 3 converts the tentative halftone estimate for the scanning aperture currently selected into the formal halftone estimate for the relevant pixel of the halftone image. The halftone estimated values are written into the memory 7 one after another to create a halftone image. 8 receives a signal from the central control means 3 indicating that official halftone estimates have been obtained for all pixels of the halftone image, reads out the halftone image in the memory 7, performs enlargement/reduction processing, and then stores the halftone image in the memory 7. This processing means binarizes the dither matrix read from 9 and stores the binary image in memory 10. Incidentally, if the plurality of memories mentioned above have a large memory capacity, a single memory is sufficient. Further, each of the above means can be easily constructed using a microprocessor.

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 the number of black pixels may be counted.

In the above description, halftones are obtained by scanning one pixel at a time, but the present invention is not limited to this, and it is also possible to scan two or more pixels at a time. Furthermore, 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 as long as the apertures have a size of one row by one column. Any type may be used. Further, the size of the scanning aperture is not limited to the size shown in the example, and any size can be used as long as it includes an aperture with a size of 1 row x 1 column.

(Effects of the Invention) As explained in detail above, according to the present invention,
Set multiple types of scanning apertures, scan the dithered image while selecting the optimal scanning aperture for each pixel using a predetermined calculation, and count the number of white or black pixels within the scanning aperture. However, by using the count value as the estimated halftone image value, an image close to the original halftone image can be obtained. According to the present invention, even if the image includes a mixture of line drawings and gradation drawings, image reproduction can be performed satisfactorily. The halftone image estimated value thus determined takes human visual characteristics into consideration, so it is closer to the original halftone image. Once the halftone image is obtained, various processes such as gradation conversion, enlargement/reduction, etc. can be performed.

[Brief explanation of drawings]

FIG. 1 is a flowchart showing one embodiment of the present invention, FIG. 2 is an explanatory diagram for obtaining a dither image from an original halftone image, FIG. 3 is a diagram showing multiple types of scanning apertures, and FIG. A diagram showing an example of the obtained estimated halftone image, FIG. 5 is a diagram showing the selection order of scanning apertures,
6 to 8 are explanatory diagrams of the method of the present invention, FIG. 9 is a diagram showing an example of an estimated halftone image obtained by the present invention, FIG. 10 is a diagram showing a selection aperture, and FIG. 11 is an enlarged FIG. 12 is a flowchart showing reduction, FIG. 12 is a diagram showing binarization processing by enlargement/reduction, and FIG. 13 is a diagram showing an example of an apparatus for realizing the method of the present invention.

Claims (1)

[Claims] 1. A scanning aperture with a size of 1 pixel x 1 pixel is placed on a pixel on a binary image consisting of a white area and a black area, which corresponds to each pixel of a halftone image to be estimated. a first step of setting a plurality of types of scan apertures including scan apertures having sizes of 2 pixels by 1 pixel and 1 pixel by 2 pixels; , count the number of white or black pixels in the image portion surrounded by this selected scanning aperture in the binary image, and further calculate the reciprocal of the area ratio of this selected scanning aperture to the maximum size scanning aperture. This counted pixel number is multiplied, the obtained value is used as a temporary halftone estimate of the pixel in the halftone image, and this temporary halftone estimate is again applied to a dither matrix of the same size as the scanning aperture. A second step of obtaining a re-binary image for the relevant pixel of the halftone image to be estimated by binarizing, and combining the re-binary image obtained in this second step with the original two-value image.
The image portion surrounded by the selected scanning aperture of the value image is compared, and when the two first become equal, the tentative halftone estimated value in the selected scanning aperture is calculated as the halftone to be estimated. a third step of creating a halftone image using the official halftone estimate obtained for all pixels of the halftone image to be estimated, which is a formal halftone estimate at the pixel of the image; The halftone image obtained in step 3 is enlarged/reduced, and then binarized using a dither matrix to obtain a binary image.
A method for enlarging/reducing a binary image, comprising the following steps: