JPH0453350B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to an image processing method for dithered images.

(Prior Art) Currently, many of the output devices in practical use, such as display devices and printing devices, can only represent binary values of white and black. Density pattern method (luminance pattern method), dither method, and the like 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, in which the number of dots recorded within a fixed area (matrix) is varied.

The density pattern method uses a threshold matrix to record multiple dots in a portion corresponding to one pixel of the original as shown in Figure 20B, and the dither method records one pixel of the original as shown in Figure 20B. In this method, the part corresponding to the image is recorded with one dot. As shown in the figures, dithered output data is obtained. This output data pseudo-represents a halftone image using white and black dots.

(Problems to be Solved by the Invention) By the way, if it is possible to restore the original halftone image (corresponding to the input data in FIG. 20) from such a binarized pseudo halftone image, it is possible to convert various data into Since the processing can be performed, it is convenient because it allows flexibility in image conversion. In the case of a density pattern image, once the pattern level arrangement is known, the image can be immediately returned to the original halftone image. However, the resolution is low compared to the amount of information. On the other hand, although a dither image has a higher resolution than a density pattern image in terms of the amount of information, it is difficult to restore the image to the original halftone image. Therefore, it has not been possible to perform various image conversions using dithered images alone.

The present invention has been made in view of the above points, and its purpose is to realize a method for estimating a halftone image of a dithered image, which can effectively estimate the original halftone image from the dithered image. be.

(Means for Solving the Problems) The present invention for solving the above-mentioned problems is such that each pixel of the halftone image to be estimated is Multiple types of scanning apertures are set for each
A dither image in which a scanning aperture that satisfies a predetermined condition is selected for each pixel from among these multiple types of scanning apertures, and a halftone image is estimated based on the number of white pixels or the number of black pixels within the selected scanning aperture. In the method for estimating a halftone image, a small scanning aperture is mainly used, and a halftone image is created based on the first dithered image in each scanning aperture and the number of white pixels or the number of black pixels in the scanning aperture. A first selection method in which a second dither image binarized by a dither matrix is compared for each scan aperture of the plurality of types of scan apertures and a unique scan aperture is selected; and determining whether the difference between the number of white pixels or the normalized number of black pixels in two types of scanning apertures having different sizes of scanning apertures is equal to or less than a certain value,
The present invention is characterized in that a second selection method of selecting a unique scanning aperture based on the determination result is used to select a unique scanning aperture for each pixel and estimate a halftone image.

(Example) Hereinafter, an example of the present invention will be described in detail with reference to the drawings. First, as one of the systematic dither methods, an example will be described in which an 8×8 Bayer matrix is used as a threshold matrix.

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

(1) Step A plurality of types of scanning apertures are set for each pixel in a dithered 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 the 8x8 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. 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 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, D has a size of 4 rows x 4 columns, and E has a size of 4 rows x 4 columns.
F has a size of 8 columns, and G has a size of 8 rows x 4 columns.
1 and 2 show scanning apertures each having a size of 8 rows and 8 columns. Here, the black circles shown in each of the scanning apertures A to G are the centers of movement when moving on the dithered image in FIG. 2C.

Incidentally, while the scanning apertures shown in FIG. 3 are kept fixed, they are moved on the dithered image shown in FIG.
If the number of white pixels or black pixels (in this case, the number of white pixels is taken) in the scanning aperture is counted and used as the estimated value of the halftone image, the estimated halftone image as shown in FIG. can get. Here, A is according to Figure 3A, B is according to Figure 3B, C is according to Figure 3C, D is according to Figure 3D, and E is according to Figure 3E.
F is a halftone image according to FIG. 3F, and G is a halftone image according to FIG. 3G.

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

Now, move the scanning aperture G defined in Fig. 3 to the initial position of the dither image (the center of movement is at the fourth
Located in the lower right corner of the 4th column of the figure. (hereinafter expressed as (4, 4)). Note that 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 values are shown with diagonal lines here for ease of viewing.

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 aperture 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, move the scanning aperture G by one pixel (one column in this case),
The number of white pixels within the scanning aperture G is counted in the same manner as described above and becomes 20. Perform the same operation for accompanying.
When the first row is completed, the scanning aperture G is set to 1.
The intermediate density estimation operation is started from the lower right corner of the pixel whose center is (5, 4) by moving to the next row. Then, the scanning aperture is moved to the last column of the last row to obtain the halftone image estimation value, and the halftone image estimation operation is completed, and the halftone image shown in FIG. 4G is obtained.

Next, a method for obtaining an estimated halftone image using the scanning aperture D shown in FIG. 4D will be explained. In this case, since it is necessary to align the movement center with the largest scanning aperture G, the movement start position of the scanning aperture D is as shown in FIG. In this state, the number of white pixels is 3, and in order to match the area to FIG. 3G, it is necessary to quadruple the number of white pixels within the aperture, so the number of white pixels is 3×4=12. In this case, the gain of the scanning aperture D is said to be 4.

Similarly, when the gains of each scanning aperture in FIG. 3 are determined, A is 4, B is 8, C is 2, E is 2, F is 2, and G is 1. Scanning aperture D
If this is performed every time one pixel is moved, an estimated halftone image shown in FIG. 4D is obtained. Figure 4 I~Ha, Ho,
The same explanation can be given for F, so the explanation 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 halftone image is estimated from the dithered image (FIG. 2C), which has a smaller amount of information than the original halftone image shown in FIG. does not return to the original halftone image. 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. especially,
When there is no change in the pixel level of the original halftone image within the scanning aperture G, the estimated halftone image level perfectly matches the original halftone 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 only a 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 Fig. 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 to 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, and the most recommended scanning aperture is selected.

Next, the method of the present invention will be explained. This method assumes that digital binary images are already stored in a 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. As the predetermined calculation process, a large aperture is selected in a low spatial frequency region (a region where there are few pixel level changes in a halftone image), and a small aperture is selected in a high spatial frequency region (a region where there are many pixel level changes in a halftone image). The algorithm used is as follows.

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 scanning aperture D as a reference aperture, using this scanning aperture D and smaller scanning apertures C, B, and A, D→C
The process is performed in the order of →B →A, and an optimal scanning aperture is selected using the first method described later. If no scan aperture is selected in the first method, scan aperture D and larger scan apertures E, F, and G are used;
Using the method described above, the optimum aperture is selected along the route D→E→G or D→F→G.

Step (1) Here, first, D is considered as the scanning aperture. Then, set the selection aperture D to (5, 6) in Figure 2 C.
When superimposed at the position (2, 3) of the designated halftone image, the result is as shown in FIG. 8A. The number of white pixels within this aperture is counted as 6. Assuming that 24, which is the number of white pixels multiplied by 6 and the gain is 4, is the average pixel level, each pixel is filled in with 24 as shown in FIG. When the average pixel level image shown in (b) is binarized using the threshold value matrix shown in (c), it becomes as shown in (d).

Here, dither image A, which is the original binary image, and re-2
Comparing value images 2 and 2 shows that 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 is a change in the pixel level of the original halftone image within the aperture D. Therefore, in this case as well, the scanning aperture D is inappropriate. Since scanning aperture D was not selected in step (1), the process proceeds to 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 D is greater than or equal to D, and the process proceeds to step.

Step (2) The scanning aperture selected in step (2) is C. Then, when the selection aperture C is superimposed on the initial position of FIG. 2C, 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, each pixel is filled in with 16 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 Become something.

Here, when dithered image H, which is the original binary image, and binary image H, which are original binary images, are compared, they have the same pattern. That is, they match. The fact that the patterns on the left and right sides 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 proceeds to step (3) of examining the next scanning aperture B.

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

Note that if they do not match, the next scanning aperture A is selected, and even if they do not match, the smallest scanning aperture A 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. Since the gain of the scanning aperture C 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 Checks whether the scanning aperture that satisfies the conditions has been determined in step.

As explained in step (1), if the original binary image in FIG. 8A and the re-binary image in FIG. Therefore, if a scanning aperture smaller than this is selected, the size of the aperture becomes smaller and the gradation quality deteriorates, so the aperture selection process ends here. Therefore,
In this case, the first method described in step cannot be used, and the following second method must be used.

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

First, we will use apertures of sizes D to G shown in FIG. 2. It is assumed that the number of white pixels in each aperture is d to g, respectively. Then, the condition that there is no change in pixel level is defined as the difference in the normalized number of white pixels in each aperture (the number obtained by correcting the number of white pixels for the difference in gain) is 1 or less. That is, it is defined as follows.

|2d−e|≦1 (1) |2d−f|≦1 (2) |2e−g|≦1 (3) |2f−g|≦1 (4) If each of these conditions is satisfied, The openings to be used are determined according to each condition, as shown in FIG. Here, the * mark in the figure indicates ○ or ×. for example,
If formulas (1) and (2) are not satisfied, there is no need to check whether formulas (3) and (4) are satisfied.
If aperture D is selected and satisfies equation (1) but does not satisfy equation (2), then aperture E is selected but does not satisfy equation (1).
When formula (2) is satisfied, aperture F is selected.
If all formulas (1) to (4) are satisfied, the aperture G is selected.

Under the above conditions, the optimum aperture will be found when the center position of each aperture in the dithered image shown in FIG. 2C is at the lower right of the (4,4) pixel. In this case, d=3, e
=9, f=8, g=21. First, conditional expression (1),
Let's try to find equation (2).

|2d-e|=|6-9|=3, so equation (1) is not satisfied; |2d-e|=|6-8|=2, so equation (2) is not satisfied. Therefore, FIG. The optimal aperture is found and becomes D according to the following. The value of the pixel in the first row and first column of the halftone image when D is selected as the aperture is estimated. When the aperture D is selected, the number of white pixels at the initial position d=3 and the gain of the aperture D is 4, so the halftone image estimated value is 3×4=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 Estimating a halftone image based on the determined scanning aperture.

1 by the first method or the second method described above.
One optimal scanning aperture is always found for each 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, the (1, 1) 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 B, (1,7)
is B, (1,8) is C, and (1,9) is C. FIG. 11B is a diagram showing all examples of selective apertures.

The estimated halftone image shown in Figure 11A is obtained by estimating a halftone image using a large aperture in areas where there are few changes in pixel level, and using a small aperture 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.

The case where a halftone image is estimated from a dithered image has been described above. This estimated halftone image is subjected to tone conversion, filtered,
A new binary image can be obtained by performing enlargement/reduction conversion.

FIG. 12 is a flowchart showing a case where tone conversion (gradation processing) is performed on an estimated halftone image. The flow shown in the figure applies tone conversion to the halftone image estimated by the present invention, and obtains a new binary image using a threshold value matrix for the converted halftone image. As the gradation conversion characteristic, the first
Something like the one shown in Figure 3 can be considered. f ₁ , f ₂ in the diagram
are tone conversion characteristic curves, where the horizontal axis is the input and the vertical axis is the output. The numbers shown in the figure are at the pixel level.

Figure 14A is a halftone image obtained by converting the gradation of the image shown in Figure 11A using the f ₁ characteristic in Figure 13;
In the figure, f is a halftone image converted to a gradation using ₂ characteristics, C is a binary image obtained by converting the image shown in A into a binary image using the aforementioned 8x8 Bayer dither matrix, and D is a binary image obtained from the image shown in B. This is a binary image that was converted into a binary image. In C and D, "1" corresponds to a white pixel and "0" corresponds to a black pixel. It can be seen that the binary images differ greatly due to differences in tone conversion characteristics.

FIG. 15 is a flowchart showing a case where an estimated halftone image is filtered. In the flow shown in the figure, a halftone image estimated according to the present invention is filtered, and a new binary image is obtained using a threshold matrix for the filtered halftone image. The first filter characteristic is
There is an example as shown in Figure 6. A is a high-pass convolution filter, and B is a low-pass convolution filter.

When the estimated halftone image shown in FIG. 11A is filtered with the characteristics shown in FIGS. 16A and 16B,
High pass as shown in Figure 17 A and B, respectively.
A low-pass halftone image is obtained. When these halftone images are binarized using the dither matrix shown in C, binary images (dither images) shown in D and E are obtained, respectively. In D and E, "1" corresponds to a white pixel and "0" corresponds to a black pixel.

FIG. 18 is a flowchart showing the case of enlarging/reducing an estimated halftone image. The flow shown in the figure enlarges and enlarges the halftone image estimated by the present invention.
A new binary image is obtained by using a threshold matrix for the halftone image that has been reduced and enlarged/reduced. As a method of enlarging/reducing, for example, an interpolation method is used.

Figure 19A shows how the halftone image shown in Figure 11A is obtained by using the simplest interpolation method, the Nearest Neighborhood method.
The halftone image is enlarged to 1.25 times, and b is the halftone image also reduced to 0.75 times. For these halftone images, the dither matrix shown in C is used to
When converted into values, binary images as shown in D and E are obtained, respectively. In D and E, “1” is a white pixel,
“0” corresponds to a black pixel.

The dithered image is preferably a dithered image based on 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 each aperture with the smallest area. A distributed dithered image such as this is preferred, and a Bayer dithered image with completely distributed threshold values is particularly preferred.

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. Further, in the above description, an example is given in which there are four types of scanning apertures, 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 from these scanning apertures through predetermined processing for each pixel. 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 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 tone conversion, enlargement/reduction, etc. can be performed.

[Brief explanation of the drawing]

FIG. 1 is a flowchart showing an embodiment of the method of the present invention, FIG. 2 is an explanatory diagram when a dither image is obtained from an original halftone image, FIG. 3 is a diagram showing multiple types of scanning apertures, and FIG. 4 5 and 6 are explanatory diagrams for estimating halftone images, FIG. 7 is a diagram showing the selection order of scanning apertures, and FIG. 8 is a diagram showing examples of estimated halftone images of each aperture obtained. An explanatory diagram of the first method, FIG. 9 is an explanatory diagram of the second method, FIG. 10 is a diagram showing the procedure for selecting a scanning aperture, and FIG. 11 is an example of a halftone image obtained by the present invention and its selection. Diagram showing an example of opening, 12th
13 is a flowchart showing gradation conversion, FIG. 13 is a diagram showing gradation conversion characteristics, FIG. 14 is a diagram showing binarization processing by gradation conversion, FIG. 15 is a flowchart showing filtering, Figure 16 is a diagram showing filter characteristics, and Figure 17 is a diagram showing filter characteristics.
Figure 18 is a flowchart showing the value conversion process, Figure 19 is a flowchart showing enlargement/reduction
FIG. 20, which is a diagram showing the digitization process, is a diagram illustrating the conventional binarization method.

Claims (1)

[Claims] 1. In the first dither image created by the dither matrix, multiple types of scanning apertures are set for each pixel of the halftone image to be estimated, and a predetermined condition is satisfied from among these multiple types of scan apertures. In a method for estimating a halftone image of a dither image, in which a scanning aperture is selected for each pixel and a halftone image is estimated based on the number of white pixels or the number of black pixels within the selected scanning aperture, a small scanning aperture is mainly used. , the first dithered image in each scanning aperture, and a second dithered image obtained by binarizing a halftone image created based on the number of white pixels or the number of black pixels in the scanning aperture using a dither matrix. , a first selection method in which a unique scanning aperture is selected by comparing each scanning aperture of the plurality of types of scanning apertures, and two types of scanning in which a large scanning aperture is mainly used and the size of the scanning aperture is different. A second selection method that determines whether the difference between the number of white pixels or the normalized number of black pixels within the aperture is equal to or less than a certain value, and selects the only scanning aperture based on the determination result; 1. A method for estimating a halftone image of a dithered image, the method comprising: estimating a halftone image by selecting a unique scanning aperture for each pixel.