JPH0418750B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to an image processing method for performing gradation processing and/or filtering of an image.

(Prior Art) There is no prior art method for processing an image based on binarized data.

(Problems to be Solved by the Invention) Since image processing of binarized data is impossible, conventionally, when the processing is incomplete, imaging is performed multiple times by changing imaging conditions. Furthermore, if the original image was not available, it was impossible to process the image.

The present invention has been made in view of the above problems, and an object of the present invention is to provide an image processing method capable of performing image processing on a binarized image.

(Means for Solving the Problems) The present invention, which solves the above problems, divides a binarized image into blocks, counts at least one of the number of black pixels or the number of white pixels for each block, and counts the number of black pixels or white pixels for each block. This method is characterized by performing gradation processing and/or film ringing on numbers.

(Example) Hereinafter, the image processing method of the present invention will be specifically explained using FIGS. 1 to 7.

First, the object of image processing is a binarized image. This binarized image can be easily obtained by using, for example, a 4×4 or 8×8 dither matrix as a threshold value. The setting width of the threshold values that make up this dither matrix is, for example, wide in the case of gradation drawings, about 0.1 to 1.4 in terms of reflection density, and narrow in the case of line drawings, in terms of reflection density, about 0.1 to 0.5 (or by setting a fixed threshold value). It is preferable to use This is to prevent the image from becoming blank or thick. Further, different types of dither matrices may be used for gradation drawings and line drawings. Note that the binarization method may be a method other than the dither method, such as a density pattern method or a dot method.

In the method of the present invention illustrated in FIG. 1, first, if the image is not a binarized image, the above-mentioned binarization is performed (step). In Figure 2, the size is 4×
The original image A (see Fig. 2, b) is binarized by the dither method using the dot dispersion type (Bayer type) dither matrix DM1 (see Fig. 2, a), as shown in Fig. 2, c. An example of obtaining a binarized image B is shown. In this figure, the dither matrix
The numbers in DM1 and the original image A indicate standardized density levels, and the pixels in the shaded area of the binarized image B indicate that they are black pixels.

In the next step, the binarized image is divided into blocks of appropriate size. In FIG. 2C, it is divided into 4×4 sizes. Then, the number of black pixels (or the number of white pixels) in each block is counted (step), and the process proceeds to gradation processing. The gradation processing performed here is
This is done by converting the above number of black pixels (hereinafter referred to as the original number of black pixels) to another number of black pixels (hereinafter referred to as the converted number of black pixels) based on a predetermined gradation curve. ). In the example of FIG. 3, the original black pixel number shown in FIG. 3B is converted to the converted black pixel number shown in FIG. 3C using the gradation curve shown in FIG. 3A. The type of gradation processing to be performed depends on the gradation curve used during conversion, but in general, the gradation curve C _A with the upwardly convex curve in Figure 4 is used. When used, the conversion increases the number of black pixels and increases the frequency of high-density areas, resulting in a gradation curve with a downward convex curve in Figure 4.
When C _B is used, the number of black pixels is reduced and the frequency of low-density portions is reduced. Therefore, the tone curve C _A
is effective for pale binarized images, and the gradation curve
C _B is effective for images that are completely black. Of course, it is also possible to use a gradation curve, such as an S-shaped curve, which is a combination of gradation curves C _{A and CB} , and it is sufficient to select a gradation curve that matches the desired gradation processing. As an image processing device, 3 to 5
It is sufficient to prepare representative gradation curves for each species and select one of them depending on the image.

Next, filtering is performed on the converted black pixel number (hereinafter referred to as the first converted black pixel number). This filtering is performed by converting the number of black pixels to another number of black pixels (hereinafter referred to as second converted black pixels) using a predetermined spatial filter (step). In the example of FIG. 5, the spatial filter shown in FIG. 5A is used to convert the first converted black pixel number shown in FIG. 5B to the second converted black pixel number shown in FIG. 5C. Note that in order to filter the outermost block, data on the number of black pixels on the outer side is also required, so for the sake of explanation here, the numbers shown in Braille are given as virtual data to perform filtering. Note that if the number of black pixels after filtering is 0 or less, it is treated as 0, and if it is 16 or more, it is treated as 16. The type of filtering that is done depends on the spatial filter that is applied during the transformation. For example, image enhancement can be performed using the spatial filters shown in FIG. 5A and FIG. 6. Here, α in FIG. 6 is a natural number of 20 or less, and β is a constant. If α is large, the edge enhancement will be quite strong.

From the number of black pixels thus obtained, the density level of each block is determined to obtain a density matrix pattern (step). Here, the block size is equivalent to the size (4 x 4 or 8 x 8) of the dither matrix (threshold value group) used to obtain the binarized image, preferably larger than the size of the dither matrix. Choose a smaller size. In this way, high resolution can be maintained while increasing the number of gradations. Figure 7(b) shows the number of second converted black pixels in each block as the normalized average density level of each block, and Figure 7(c) shows each block based on the number of second converted black pixels of each block. The image is reconstructed by copying the density matrix pattern onto the block, and the determination of the first density pattern in this example is as follows:
This is done by comparing the density level of each block with a dither matrix DM2 (see FIG. 7A), which is the same as the dither matrix DM1 described above (step). For example, in the case of block BK1, its density level is 9, so in the dither matrix DM2 in FIG.
A density matrix pattern like BK1 will be created.

Note that matrices DM1 and DM2 do not need to be composed of the same thing; for example, if matrix DM1 and DM2 are
2 may be of dot concentration type (spiral type). Furthermore, the order of gradation processing and filtering may be interchanged, or only one of them may be performed.

(Effects of the Invention) As explained above, in the present invention, a binarized image is divided into blocks, the average density level (number of black pixels or number of white pixels) is determined, and then gradation processing and/or filtering is performed. As a result, it has become possible to perform image processing on binarized images.

[Brief explanation of drawings]

FIG. 1 is a flowchart showing an example of the method of the present invention, FIG. 2 is an explanatory diagram showing an example of binarization in FIG. 1, FIG. 3 is an explanatory diagram of gradation processing in FIG. 1, and FIG. is an explanatory diagram of a gradation curve, FIG. 5 is an explanatory diagram of filtering in FIG. 1, FIG. 6 is an explanatory diagram of a spatial filter, and FIG. 7 is an explanatory diagram of a method for obtaining a density matrix pattern for image reconstruction. It is. DM1, DM2...Dither matrix, A...
...Original image, B...Binarized image, C _A , C _B
...gradation curve.

Claims (1)

[Claims] 1 A binarized image is divided into blocks, at least one of black pixels and white pixels is counted for each block, and gradation processing and/or filtering is applied to the number of pixels. Image processing method.