JPS62117470A - Enlargement and reduction method for binary picture - Google Patents

Abstract

PURPOSE:To obtain a picture close to an original halftone picture by scanning a dither picture from the plural types of scan openings while an optimum scan opening is selected at every picture element and setting the number of white picture elements in the scan opening to an estimated halftone picture. CONSTITUTION:The plural types of scan consisting openings B, C and D including a one with one picture element X one picture element are set in a binary picture consisting of white and black areas at every picture element of the estimated halftone picture. The binary picture in each scan opening is compared with the one generated from the estimated value based on a ratio between the white and black areas in the opening at every scan opening, and the estimated value 4 of the optimum scan opening B is obtained at every picture element of the halftone picture to be estimated. Based on the estimated value 4, the halftone picture is generated, enlarged and reduced, and then binarized.

Description

[Detailed description of the invention] (Industrial application field) 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.

(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 Replace pixel a polish ■ r with the nearest 4 original pixels △, B.

The concentrations Ia, Ib, Ic, (cl) of C and D are output as the logical sum of 1b. That is, Ir-Ia Ulb (, Jrc Uld
(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 G1 to G8, and create partial areas that include the converted pixels! iii! The converted pixel wetness level 1r is determined using a predetermined logical operation formula determined according to Qm (m-1 to 9). For example, when m = 13, ir can be found using the following equation.

Ir-TdLノIa
(2) ■ Calculate the average density fr of the original pixel projected onto the projection method converted pixel, and perform darkness value processing on that value to obtain the converted pixel value Ir.

(Problems to be solved by the invention) SF) In the case of the C method, the processing [is accurate, but the stroke tends to become thinner when reduced, and the connection between the strokes and the strokes is lost. (There is a problem that J is likely to occur. In the case of the disjunction method), the lines become thicker, but when reducing the size, unnecessary connections of the lines occur and the image becomes noticeably bulky. There is a problem that the 9-division method makes the stroke thicker when reducing the image.
, (although it is possible to improve the visibility of 9 - F,
There is a problem in that the stroke becomes thicker even during enlargement conversion, which deteriorates the image quality. In the case of the projection method after R, the converted image is less 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 an Iser image is enlarged or reduced using conventional techniques, there are disadvantageous phenomena such as occurrence of moiré 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 binary images such as characters and line drawings that are essentially only burnt white, and dithered images that express halftones in a pseudo manner. Binary image enlargement/reduction that allows both to be enlarged/reduced well.
The goal is to realize a reduction method. 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, which solves the above-mentioned problems, has the advantage of at least one pixel x one pixel for each pixel of a halftone image that should be estimated in a binary image consisting of a white area and a black area. Multiple types of scanning apertures, including those with a size of Compare each scanning aperture, find the optimal scanning aperture nt setting for each pixel of the halftone image to be estimated, create a halftone image based on the estimated value, and enlarge this halftone image. - It is characterized in that it is binarized after performing reduction processing.

(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 once returns a binary image to a halftone image, enlarges/reduces one intermediate image,
The image is dinitrated 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 is adopted in which JξriJ and η− are kept black.

(Embodiments) Hereinafter, embodiments 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. The following will explain one foot of this broach.

Step (2) A plurality of types of apertures, including those 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.

First, an example will be described in which a 4×4 Bayer type matrix is used as a threshold matrix as one of the systematic dither methods.

FIG. 2 is a diagram showing an example of a matrix for explaining the present invention. (A) is the A original halftone image converted to digital data, (B) is a 4x4 Bayer dither threshold matrix, (C) is converted to a black and white binary image (dither image) using the threshold matrix (B) This is a dithered image (binary image) of the original image (a). Here, the Bayer type thresholding 71-lix has a dither pattern in which dots are dispersed as shown in Figure (B).

Figure 3 shows multiple types of scanning apertures (unit areas) used in the present invention.
FIG. Z is △ of size 1 row x 1 column
has a size of 2 rows x 2 columns, B has a size of 2 rows x 4 columns,
C indicates a scanning aperture having a size of 4 rows by 2 columns, and D indicates a scanning aperture having a size of 4 rows by 4 columns. In this embodiment, as shown in Figure J, the scanning amount E is 2 rows x 1 column and 1 row x 2 columns.
is not used because it has poor image restoration characteristics for images in which character images and gradation images are mixed.

Here, the black circles shown in each scanning amount LJ of Z and Δ-D are
This is the center of movement when moving the "f" image in FIG. 2(c). Note that both rows and columns correspond to pixels. For example, 1 corresponds to the size of 2 rows x 4 columns and 12 pixels x 4 pixels, and the scanning amount is 1''.The reason why we chose -1 pixel x 1 pixel is because character images and gradation images are mixed. This is to reproduce and cover character images from images in a good manner. The present invention selects one of the maximum 38 openings among these multiple types of frontages,
The most [3! When selecting an ideal frontage, it is necessary to consider the following: 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 to express high gradation in the low spatial frequency region, and a small aperture is used to reproduce a high resolution image in the high spatial frequency region, a high-quality halftone image can be obtained as a rest. Obtainable. By the way,
While keeping the scanning apertures shown in Fig. 3 fixed, they were moved on the dip image shown in Fig. 2 (c), and the number of white pixels or black pixels in the scanning aperture was calculated. When the estimated value of the halftone image is counted, estimated halftone images as shown in FIGS. 4(A) to 4(E) are obtained. Here, (a) is based on Figure 3 7, (b) is based on Figure 3,
(C) is based on Figure 3B, (D) is based on Figure 3C, (
E) are halftone images based on the third drawing.

Step ■ First, select the maximum aperture.

As explained in step (2), the basic idea of the present invention is to select the widest possible width as long as no change in density of the A original halftone image is observed within the gap L1. Therefore, here, the order of selection is shown in FIG. The method for obtaining the halftone image shown in FIG. 4(E) will now be explained.

Now, move the scanning aperture defined in (e) to the initial position of the dithered image (the position where the center of movement is at the bottom right of the second row and second column, hereinafter expressed as (2, 2)) as shown in Figure 6. Overlap. In this case, it is desirable that each pixel contained within the scanning aperture as shown is completely contained. 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. The number of white pixels within the scanning aperture in the state shown in the figure is 7.

Therefore, halftone estimation ii! The 1st row and 1st column of the ii statue (1, 1
The estimated value of > is 7. Next, by moving the scanning amount [1 by one pixel, in this case one column], and counting the number of white pixels in the scanning interrogation in the same manner as described above, it becomes 7.

Perform the same operation for accompanying.
When this is completed, the scanning aperture is moved by one line to the next line, and the intermediate temperature estimation operation is started from the lower right of the pixel whose center is (3, 2). Then, the scanning aperture is moved to the column after R in 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.
It will be done.

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

In this case, since it is necessary to align the movement center with the largest scanning aperture, 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 the third diagram, it is necessary to double the number of white pixels in the aperture, so the number of white pixels is 2X2 = 4. .

In this case, the gain of the scanning aperture B is said to be 2.

In the same way, the gain of each scanning aperture in Fig. 3 is calculated as follows:
7 is 16, A is 4, and C is 2. If such calculation is performed every time the scanning aperture B is moved by one pixel, the result shown in Fig. 4 (C) is obtained.
The constant halftone image shown in 11] is converted to 1[1]. 4 (a), (b), (b), and (2) can be considered in the same way, so their explanations will be omitted.

As described above, the image method J is capable of 1) estimating a halftone image relatively well; The data in FIG. 4 is a diagram showing the eftft halftone image obtained in this manner. Of course, in such a method, the halftone image is estimated from the dithered image (FIG. 2(C)), which has a smaller amount of information than the original halftone image shown in FIG. As shown in E), the image does not completely return to the original halftone image of 11. However, unless the pixel level of the original halftone image changes rapidly,
The original halftone image is approximated and the halftone image is distorted. In particular, if there is a change of 1α in the scan amount I-ID, the estimated lζ halftone image value completely matches the A rigid norm halftone image value. [7
．． Due to the principle as above, low spatial frequency l! ! ! In Ryotetsu, high gradation is expressed using a large scanning aperture, and in the high spatial frequency region, if a high resolution image is reproduced using a small image [circle [=1], the halftones shown in Fig. 4 are reproduced. It is possible to estimate a halftone image even better than the image estimation value.

Step (2) Obtain an estimated value based on the ratio of the white area to the black area within the selected opening, and re-binarize this estimated value using a dither matrix corresponding to the serrations of the opening.

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. 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 uses an algorithm that selects a large aperture in a low spatial frequency region (region with few pixel level changes) and a small aperture in a high spatial frequency region (region with many pixel level changes). is used.

This will be explained using FIG. Here, as described above, first, D is selected as the scanning aperture. Then, if the selected width is superimposed on the initial position of FIG. 2 (c) (see FIG. 6), four options are obtained as shown in FIG. 8 (a). The number of white pixels within this aperture is 7. Assuming that this number of white pixels, 7, is the average pixel level, each pixel is padded with 7 as shown in (b). When the ST1 equal pixel shown in (b) and the Bell image are binarized and overturned with the equivalence matrix shown in (c), a binary image @ (re-2 tff 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) shown in (A) and the re-binary image (D) will be compared. As is clear from the figure, patterns (a) and (ii) do not match.The fact that patterns (a) and (d) are not the same pattern means that there has been a pixel level change. In this case, the scanning aperture is inappropriate.

■ Since scan amount 1''D was not selected for culm (1), proceed to step ■.

Step ■ Select the next scan aperture. Here, the scanning amount DC is selected next in the scanning aperture selection order shown in FIG. Then, perform steps ■ and ■ again. When the selected frontage 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 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 (v). In response to (f), the average pixel level image is expressed as a binary value by the threshold matrix shown in (g) (consisting of the threshold matrix in Figure 2 (b) and the second and third columns, that is, the threshold matrix within the scanning aperture). When converted into a binary image, a re-binary image as shown in (H) is obtained. Here, when comparing the original binary image (E) and the re-binary image (B), they are not the same pattern. In other words, there is a mismatch. (E)
The fact that the patterns in and (h) are not the same pattern means that there has been a pixel level change. Therefore,
In this case as well, the scanning amount 1-]C is inappropriate.

The scanning amount rII C was selected in T culm (2) and it was 4r, so step @, again press (?) 3゜The next selected scanning amount [[1 is d by 13. Yes,
If you set the selected frontage to the first 11) f◇ position in Figure 2 (c), the result will be as expected. The number of white pixels within this aperture is 1 to 2 when covered. Add a gain of 1 to this number of white pixels.
Assuming that y and 4 are the average pixel L novel, each pixel is padded with 4 as shown in (J).

When the average pixel level image shown in (J) is binarized using a Hobb threshold matrix (L), a binary image as shown in (E) is obtained. Here, when comparing the original binary image @(s) and the re-binary image (71), the pattern is the same. That is,
Both patterns match. The fact that the patterns in (a) and (ii) are the same pattern means that there is no change in pixel level within this scanning aperture. Therefore, in this case, scanning aperture B is appropriate. If they do not match, the same operation is repeated until the last scanning aperture (7 in this case). That is, the final scanning amount = 15
- Mouth Z becomes the scanning aperture to be sought.

Step (2) Use the estimated value obtained from the matched scanning aperture as the halftone image &11f constant value of the pixel.

When the scanning aperture B is selected as described above, the number of white pixels within the scanning aperture B is 2 as described above. Since the gain of the scanning aperture B is 2, the estimated image value to be obtained is 2X2=4. That is, the pixel level shown in (b) directly serves as the image estimate value. Perform the above operations in the second step.
When this is performed for each pixel of the dithered image (binary image) shown in FIG. 9(C), an estimated halftone image as shown in FIG. 9 is obtained.

Incidentally, to explain which scanning aperture was used for each halftone image estimation, taking the case of the first row as an example, (1, 1) of the halftone estimation image is 8, (1, 2> is B, ( 1, 3) is B
, (1,4,) is A, (1,5) is A, (1,6) is 8
．． (1,7) is D.

FIG. 10 is a diagram showing all selected frontages.

The estimated halftone image shown in FIG.
In areas with many changes in pixel level, a small scanning amount is used.
Since the halftone image is fixed at 111 using -1, it is in line with human visual characteristics. Therefore, Jf [
The room halftone image is extremely close to -C to the original halftone image shown in FIG. 2(A).

Step (2) Perform enlargement/reduction processing on the obtained halftone image, and re-binarize the halftone image after enlargement/reduction.

FIG. 11 is a flowchart showing the case of enlarging/reducing an estimated halftone image. The flow shown in the figure enlarges/reduces the halftone image estimated by the present invention, and uses a threshold matrix to create a new four-tone image for the enlarged/reduced halftone image.
[This is to obtain a binary image. As a method of enlarging/reducing, for example, an interpolation method is used.

FIG. 12(A) shows a halftone image similar to FIG.
rhoocl method), J is a halftone image enlarged by 1.25 times, and (b) is a halftone image reduced by the same <0.75 times. When these halftone images are re-binarized using the dither matrices shown in (c) and (d), respectively, re-binarized images as shown in (e) and (kube) are obtained.

The dithered image is preferably a dithered image using a systematic dithering method so that one threshold value is entered at each frontage of the largest area than random dithering or striped dithering, and a dark value is also applied to the frontage of a small area. A distributed dither image in which the threshold values are evenly distributed is preferable, and a Bayer dither image in which the threshold values are completely distributed is particularly preferable.

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 two or more pixels may be scanned at a time.

In addition, in the above explanation, four types of scanning apertures are used.
Although the case of the type is taken as an example, the present invention is not limited to this case.
, t 'l <, 1 tr
Any type may be used as long as it includes. Furthermore, the amount of scanning [ ] need not be limited to the example shown, and can be as large as 1 line x
Any size can be used as long as it includes a frontage the size of one row.

(Effects of the Invention) According to the present invention described in detail above, a plurality of types of scanning apertures are set, and from these scanning amounts [], the optimum scanning amount [] is determined for each pixel by a predetermined oil cylinder Scan the dithered image while selecting -1 and count the number of self-pixels within the scanning aperture. 1 image
! 7. According to the present invention, it is possible to perform image reproduction satisfactorily even in images where line drawings and gradation drawings are mixed. Since the characteristics are taken into account, the resulting image is closer to the original halftone image. Once a halftone image is obtained, extensive processing 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, and FIG.
The figure is an explanatory diagram when one dither image is extracted from the original halftone image, Figure 3 is a diagram showing multiple types of scanning apertures, Figure 4 is a diagram showing an example of the obtained estimated halftone image, and Figure 5 6 to 8 are diagrams illustrating the selection order of scanning apertures, FIGS. 6 to 8 are explanatory diagrams of the method of the present invention, FIG. Diagram showing selection frontage, No. 11
The figure is a flowchart showing enlargement/reduction, and FIG. 12 is a diagram showing binarization processing by enlargement/reduction. Patent applicant Roku Konishi Photo Industry Co., Ltd. Agent Patent attorney Fuji Ijima 1 person Figure 2 Figure 3 (C) (D) AI Figure 4 (E) Opening D Figure 11 Corners etc. 12 (B) (B) (C)
(d) Dither matrix
Dither matrix (E)
(to) Shi version - enlarged dithered image

Claims (7)

[Claims] (1) Multiple types of scanning apertures, including those with a size of at least 1 pixel x 1 pixel, are set for each pixel of the halftone image to be estimated in a binary image consisting of a white area and a black area, and each scan The binary image within the aperture is compared with the binary image created from the estimated value based on the ratio of the white area to the black area within the aperture for each scanning aperture, and each pixel of the halftone image to be estimated is calculated. The present invention is characterized in that an estimated value of an optimal scanning aperture is determined, a halftone image is created based on the estimated value, and the halftone image is subjected to enlarging/reducing processing and then binarized. How to enlarge/reduce binary images. (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.