JPH0453349B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a method for estimating a halftone image of a binary image. The present invention relates to a halftone image estimation method for binary images that can be estimated.

(Prior Art) Currently, many of the output devices in practical use, such as display devices and printing devices, can only be represented by 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 on a portion corresponding to one pixel of the document, as shown in FIG. This is a method of recording corresponding parts with one dot. As shown in the figures, binarized output data is obtained. This output data pseudo-expresses a halftone image using binary values of white and black.

(Problem 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. 17) 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, it is possible to immediately restore 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 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 an object of the present invention is to provide a halftone image of a binary image that can satisfactorily estimate the original halftone image from a binary image (for example, a dither image). The objective is to realize an estimation method.

(Means for Solving the Problems) The present invention which solves the above-mentioned problems is such that pixels of a halftone image to be estimated are mixed with pixels of the binary image in a binary image consisting of white pixels and black pixels. A plurality of types of scan apertures are set at the same pitch and for each pixel of the halftone image to be estimated, and a predetermined scan aperture is selected for each pixel from among these multiple types of scan apertures. A binary pixel halftone image estimation method for estimating a halftone image based on the number of white pixels or the number of black pixels within a selected scanning aperture, the method comprising: estimating a halftone image based on the number of white pixels or the number of black pixels within a selected scanning aperture; When selecting an aperture, consider the horizontal pixel level change, which is the difference in the number of white pixels or black pixels within the same aperture arranged horizontally adjacent to each other with the relevant pixel at the center, and The vertical pixel level change, which is the difference between the number of white pixels or the number of black pixels in the same aperture arranged as shown in FIG. For the direction in which the change is less than a certain value, the aperture in that direction is large, and in the direction in which the pixel level change exceeds the certain value, the aperture in that direction is small. It is characterized in that it is selected from among the apertures.

(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 a 4×4 Bayer matrix is used as a threshold matrix.

FIG. 1 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. 2 is a diagram showing an example of a plurality of types of openings (unit areas) used in the present invention. A has a size of 2 rows x 2 columns, B has a size of 2 rows x 4 columns, and C has a size of 4 rows x 4 columns.
D indicates an opening having a size of 2 columns and 4 rows x 4 columns. Here, the black circles shown in each of the apertures A to D are the centers of movement when moving on the dithered image in FIG. 1C. By the way, while keeping the apertures shown in Figure 2 fixed, we moved them on the dithered image in Figure 1C and counted the number of white pixels or black pixels in the apertures (here we took the number of white pixels). If this is used as the estimated value of the halftone image, estimated halftone images as shown in FIGS. 3A to 3D are obtained. Here, A is based on Figure 2 A, B is based on Figure 2 B,
C is a halftone image according to FIG. 2C, and D is a halftone image according to FIG. 2D.

A method for obtaining the estimated halftone image shown in FIG. 3D will be explained.

Now, the aperture defined in step D is superimposed on the initial position of the dithered image (the position where the center position is in the second row and second column, hereinafter expressed as (2, 2)) as shown in FIG.
In this case, the pixels contained within the aperture as shown in the figure are
It is desirable that each is fully included. That is,
It is desirable to prevent a 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 this aperture is counted and that value is used as the estimated value of the halftone. In the state shown in the figure, the number of white pixels within the aperture is 7. Therefore, the estimated value of the first row and first column (1, 1) of the estimated halftone image is 7. Next, the aperture is moved by one pixel (in this case, one column) and the number of white pixels within the aperture is counted in the same manner as described above, resulting in 7. Perform the same operation for accompanying.
Then, when the first row is completed, move the aperture D by one row to the next row, start the intermediate density estimation operation from the position whose center is (3, 2), and move the aperture D to the next row by one row, start the intermediate density estimation operation from the position where the center is (3, 2), The value of is obtained. Then, the 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. 3D is obtained.

Next, a method for obtaining an estimated halftone image using the aperture B shown in FIG. 3B will be described. In this case, since it is necessary to align the movement center with the largest aperture D, the movement start position of the 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. 2, 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 aperture B is said to be 2. Similarly, when the gain of each aperture in FIG. 2 is determined, A is 4 and C is 2. If such a calculation is performed every time the aperture B is moved by one pixel, the estimated halftone image shown in FIG. 3B is obtained. 3A and 3C can be considered in the same way, so their explanation will be omitted.

Halftone images can also be estimated relatively well using the method described above. The data in FIG. 3 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 (c), which has a smaller amount of information than the original halftone image shown in FIG. It does not completely 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 density change within the aperture, the estimated halftone image value perfectly matches the original halftone image value.

By the way, human vision has a high gradation discrimination ability in low spatial frequency areas (areas with few pixel level changes), but only a low pixel level gradation discrimination ability in high spatial frequency areas (areas with many pixel level changes). It has the characteristic that there is no Therefore, if high gradation is expressed using a large aperture in the low spatial frequency region, and an image with high resolution is reproduced using a small aperture in the high spatial frequency region, the estimated halftone image shown in Figure 3 It is possible to estimate a halftone image even better than the above.

The method of the present invention will be specifically explained below. first,
Taking as an example the case of the pixel in the first row and first column of the dithered image shown in FIG. 1C, how to select an aperture from among the plurality of types of apertures shown in FIG. Here, the condition that there is no change in pixel level is determined as shown in the following equation.

|B ₁ −B ₂ |≦1 (1) |C ₁ −C ₂ |≦1 (2) Here, the relationship among B ₁ , B ₂ , C ₁ , and C ₂ is as shown in Figure 6. becomes. That is, B ₁ and B ₂ are the numbers of white pixels in each of the adjacent apertures B shown in FIG. 2, and C ₁ and C ₂ are the numbers of white pixels in each of the adjacent apertures C shown in FIG. It is a number.

Arrange B ₁ , B ₂ , C ₁ , and C ₂ as shown in Figure 6,
Check whether conditional expressions (1) and (2) are satisfied. A case where the condition is satisfied is indicated by a circle, and a case where the condition is not satisfied is indicated by an x, and the aperture to be used is determined according to each condition as shown in FIG. Explaining FIG. 7, if both equations (1) and (2) are satisfied, this means that the pixel level changes are small, so it is necessary to use a large aperture to express high gradations. Therefore,
D with a large aperture is selected. Next, if formula (1) is satisfied but formula (2) is not satisfied, this means that there is little pixel level change in the vertical direction (Y direction in Figure 6) and many pixel level changes in the horizontal direction. If C is selected that is large in the Y direction and satisfies equation (2) but does not satisfy equation (1), the pixel level changes in the horizontal direction (X direction in Figure 6) and the pixel level changes in the vertical direction. Since this is a case where there are many numbers, B, which is large in the X direction, is selected. Finally, if both equations (1) and (2) are not satisfied, the pixel level changes in the vertical and horizontal directions (X, Y directions).
In this case, A with the smallest aperture for obtaining high resolution is selected.

According to the method described above, the present invention selects an optimal aperture for each pixel, counts the number of white pixels (or the number of black pixels) within the aperture, and repeats halftone image estimation values. By the way, if we take the case of the upper left corner of Figure 1C shown in Figure 6 as an example, B ₁ = 2, B ₂ =
5. Since C ₁ =3 and C ₂ =4, equation (2) is satisfied, but equation (1) is not satisfied. Therefore, if the optimum aperture is selected according to the selection shown in FIG. 7, B will be obtained.
If B is placed at the initial position as shown in Figure 5,
Since the number of white pixels in the area is 2 and the gain of B is 2 times, the halftone image estimated value for the position of the dithered image (2, 2) is 2×2=4. Next, the aperture is similarly determined for the position centered at (2, 3), and an estimated value is obtained. In this way, the aperture is determined for each position and an estimated value is obtained.

FIG. 8 is a diagram showing an example of an estimated halftone image obtained in this manner. By the way, to explain which 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 B, (1, 2) is B, (1,3) is B, (1,4)
is A, (1,5) is C, (1,6) is D, (1,7)
is D.

The estimated halftone image shown in Figure 8 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. 1A.

Above, we have explained the case of estimating a halftone image from a binary image, but by applying tone conversion, applying a filter, or enlarging/reducing the estimated halftone image, new A binary image can be obtained.

FIG. 9 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 0 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 11A shows the image shown in Figure 8 as f ₁ in Figure 10.
The halftone image after tone conversion using the characteristics, B is the one shown in Figure 10.
Halftone image converted to gradation using _f2 characteristics, C is a binary image obtained by binarizing the image shown in A, and D is a binary image obtained by converting the image shown in B using the aforementioned 4×4 Bayer dither matrix. This is a binary image that has been converted into a binary image. It can be seen that the binary images differ greatly due to differences in tone conversion characteristics.

FIG. 12 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 3. A is a high-pass convolution filter, and B is a low-pass convolution filter.

The estimated halftone image shown in FIG.
When applied to the characteristic filters shown in Figures A and B, high-pass and low-pass halftone images as shown in Figures A and B, respectively, are obtained. When these halftone images are binarized using the dither matrices shown in C and D, respectively, binary images (dither images) shown in E and F are obtained.

FIG. 15 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 16A shows the method for converting the halftone image shown in Figure 8 using the Nearest Neighborhood method.
(neighborhood method), the halftone image is enlarged by 1.25 times, and b is the halftone image also reduced by 0.75 times. When these halftone images are binarized using the dither matrices shown in C and D, respectively, binary images shown in E and F are obtained. Further, in the above description, an example is given in which there are four types of openings, but the present invention is not limited to this, and any type may be used.
Further, the size of the opening does not need to be limited to the illustrated one, and any size can be used.

(Effects of the Invention) As explained in detail above, according to the present invention,
In a binary image consisting of white pixels and black pixels, pixels of a halftone image to be estimated are set at the same pitch as the pixels of the binary image, and a plurality of types are set for each pixel of the halftone image to be estimated. A scanning aperture is set, a predetermined scanning aperture 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 this case, when selecting a predetermined scanning aperture from among the plurality of types of scanning apertures in each pixel, the number of white pixels or black pixels in the same aperture arranged horizontally adjacent to each other with the corresponding pixel as the center is selected. The horizontal pixel level change, which is the difference in the number of pixels, and the vertical pixel level change, which is the difference between the number of white pixels or black pixels in the same aperture arranged adjacent to each other in the vertical direction, are calculated. It is determined whether the pixel level change in the direction is less than a certain value, and if the pixel level change is less than a certain value, the aperture in that direction is larger, and if the pixel level change exceeds a certain value, the aperture in that direction is larger. Since a small scanning aperture is selected from among the plurality of types of scanning apertures as the scanning aperture of the corresponding pixel, the scanning aperture with the optimum size is selected in both the vertical and horizontal directions, thereby creating an original halftone image. It is possible to obtain an image close to . 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 the drawing]

FIG. 1 is an explanatory diagram when a dither image is obtained from an original halftone image, FIG. 2 is a diagram showing multiple types of apertures, FIG. 3 is a diagram showing an example of the obtained estimated halftone image, and FIGS. FIG. 7 is an explanatory diagram of the method of the first embodiment, FIG. 8 is a diagram showing an example of an estimated halftone image obtained by the method of the first embodiment, FIG. 9 is a flowchart showing gradation conversion, and FIG. FIG. 10 is a diagram showing gradation conversion characteristics, FIG. 11 is a diagram showing binarization processing by gradation conversion, FIG. 12 is a flowchart showing filtering, FIG. 13 is a diagram showing filter characteristics,
Fig. 4 is a diagram showing binarization processing by filtering, Fig. 15 is a flowchart showing enlargement/reduction, Fig. 16 is a diagram showing binarization processing by enlargement/reduction, and Fig. 17 is a conventional binary processing. FIG.

Claims (1)

[Claims] 1. In a binary image consisting of white pixels and black pixels, pixels of a halftone image to be estimated are set at the same pitch as the pixels of the binary image, and a plurality of pixels are set for each pixel of the halftone image to be estimated. A predetermined scanning aperture is selected for each pixel from among the plurality of scanning apertures, and a halftone image is created based on the number of white pixels or the number of black pixels within the selected scanning aperture. In the halftone image estimation method for binary pixels to be estimated, when selecting a predetermined scanning aperture from among the plurality of types of scanning apertures for each pixel, the scanning apertures are selected so that they are horizontally adjacent to each other with the corresponding pixel at the center. Horizontal pixel level change, which is the difference in the number of white pixels or black pixels in the same aperture, and vertical pixel level change, which is the difference in the number of white pixels or pixels in the same aperture, which are arranged adjacent to each other in the vertical direction. Find the direction pixel level change, judge whether the pixel level change in each of these vertical and horizontal directions is less than a certain value, and if the pixel level change is less than a certain value, the aperture in that direction is large and the pixel level change is constant. A method for estimating a halftone image of a binary image, characterized in that in a direction exceeding a value, a scanning aperture having a small aperture in that direction is selected from among the plurality of types of scanning apertures as the scanning aperture of the corresponding pixel.