JPH0691605B2 - Image processing device - Google Patents

Description

The present invention relates to an image processing apparatus for processing an image with a digital signal.

[Prior Art] Generally, a so-called digital copying apparatus that samples an image by a CCD sensor or the like and outputs digitized data from a digital printer such as a laser beam printer to reproduce the image is a conventional analog copying due to the development of digital equipment. It is becoming widespread instead of the device.

This digital copying apparatus generally adopts a method of reproducing gradation by a dither method or a density pattern method for reproducing a halftone. However, this method has the following problems.

(1) When the original is a halftone image such as a print, a periodic striped pattern which is not in the original appears in the copied image.

(2) If the original contains line drawings, characters, etc., the edges are cut off by the dither processing, and the image quality is degraded.

The phenomenon of (1) is called a moiré phenomenon, and its cause is considered to be (A) a beat due to a halftone dot original and input sampling, and (B) a beat between a halftone original and a dither threshold matrix.

In particular, in the phenomenon of (B), when the threshold values of the dither are arranged in a dot-concentrated manner, the output image also has a pseudo halftone dot structure, which causes a beat with the input halftone dot original to cause moire. It causes a phenomenon.

On the other hand, there is an error diffusion method as another binarization method.
This method is a method in which a density difference between the image density of an original document and an output image density is calculated for each pixel, and an error resulting from the calculation is distributed to a peripheral pixel after specific weighting. For this, see RWFloyd and L. Steinberg.
"AnAdptive Algorithm for Spatial Gray Scale" SID
75 Digest, PP.36-37, 1975.

[Problems to be Solved by the Invention] Since the error diffusion method described above has no periodicity in processing, moire does not occur in a halftone image. However, there are drawbacks such that a peculiar striped pattern is generated in the output image and grainy noise is conspicuous in the highlight portion and the dark portion of the image.

The present invention eliminates the above-mentioned drawbacks of the prior art, and detects edge components of an image when weighting and distributing error data generated by binarization processing to a plurality of pixels based on a weighting coefficient. An object of the present invention is to provide an image processing device capable of sharply reproducing an edge portion of an image by changing the weight ratio of the weight coefficient with respect to a plurality of pixels.

[Means for Solving the Problem] In order to solve this problem, the image processing apparatus of the present invention is
Input means for inputting image data, binarizing means for binarizing the input image data into binary data, and computing means for computing error data generated by the binarizing processing in the binarizing means. Distribution means for distributing the error data obtained by the computing means to the image data of a plurality of pixels that have not been binarized by weighting them based on weighting factors, and a detecting means for detecting an edge component of the image. The distribution means changes the ratio of the weight of the weight coefficient to the plurality of pixels according to the detection result of the detection means.

[Operation] With such a configuration, by changing the ratio of the weight to the distribution of the error data to the plurality of pixels according to the edge component of the image, the halftone image is preferably binarized, and the edge portion of the image is Can be reproduced sharply.

[Embodiment] An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram of the image processing apparatus of this embodiment. Image data read by the input sensor unit 1 having a photoelectric conversion element such as a CCD and a driving system for scanning the same is sequentially sent to the A / D converter 2. Here, for example, the data of each pixel is converted into 8-bit digital data. As a result, the data is quantized into data having 256 levels of gradation. Next, in the correction circuit 3, corrections such as a shading correction for correcting the sensitivity unevenness of the sensor and the illuminance unevenness due to the illumination light source are performed by digital calculation processing.

Next, the corrected image data 100 is sent to the error correction circuit 4 together with the error data 300 output from the error calculation circuit 6. Here, the image data 100 and the weighted error data 300 are added together. The added data is sent to the binarization circuit 5 as error correction data 200, where it is compared with a threshold value T and binarized. Threshold T
Is, for example, T = 1 if the error correction data 200 is 8 bits.
Although it is set to 27, other suitable values may be used. The binarized data is sent to the printer 7 and the error calculation circuit 6.

The error calculation circuit 6 calculates the error between the data before binarization and the data after binarization, and sends it as error data 300 to the error correction circuit 4. The printer 7 converts the binary signal into a dot ON / OFF signal to form an image.

FIG. 2 is a block diagram showing an example of the error correction circuit 4. The image data 100 corrected by the correction circuit 3 is sent to the edge detection circuit 8 and the adder 9. The edge amount is calculated in the edge detection circuit 8 and sent as edge amount data 101 to the weighting generator 10 which stores the weighting coefficient corresponding to the edge amount data 101. The weight generator 10 is composed of look-up tables 10a to 10d. In the adder 9, the image data 100 and the lookup table 10a-10
The weighted error data 300 from d is added and output as error correction data 200.

For the lookup tables 10a to 10d, the edge amount data 10
The error data 300 output from the line buffer 11 is weighted according to 1. Weighting is performed by the following formula.

Here, εij is error data, Eij is weighted error data, α is the amount of edge, and αm (γ) (m = 1,2,3,4) is a weighting function with γ as a parameter.

_{E 1, ij = {α 1} (γ) εi - j, j -1} / β ( output from _{10a) E 2, ij = {} α 2 (γ) εi, j -1} / β ( from 10b _{output) E 3, ij = {α} 3 (γ) εi + j, j -1} / β ( output from _{10c) E 4, ij = {} α 4 (γ) εi - j, j} / β (10d Output from) In this embodiment, α ₁ (γ) = α ₃ (γ) = 1 α ₂ (γ) = α ₄ (γ) = [(α / 30) +1] where [] is a Gaussian symbol.

Although the error from the four pixels around the pixel of interest is calculated here, the error from n pixels around the pixel of interest can be considered by increasing the lookup table. Also, the weighting function αm (γ) can take different functions, and is not limited to the above embodiment. FIG. 8 shows α in the above embodiment.
m (γ) (m = 1,2,3,4) is shown. FIG. 10 shows a target pixel and four surrounding pixels.

FIG. 3 is a block diagram showing an example of the edge detection circuit 8. Regarding the corrected image data 100, one of the line buffer memories 21a to 21d selected by the selector 20 is being written and the remaining three are being read. When the image data 100 is completely written in the first line buffer 21a, the next data is written in the second line buffer 21b. Data is sequentially written into the third and fourth line buffers 21c and 21d, and when the writing into the fourth line buffer 21d is completed, the data is written back to the first line buffer 21a.

As a result, the line buffers 21a to 21d record data of three consecutive lines before the line data of the image currently being written, and the selector 22 selects and reads the data. This line data is sent to the maximum value detection circuit 23 and the minimum value detection circuit 24. Here, the line buffers 21a to 21d are shown in the figure using four, but may be six, for example, and the number is not limited.

The maximum value detection circuit 23 obtains the maximum value from the line data, and the minimum value detection circuit 24 obtains the minimum value from the line data and sends it to the subtraction circuit 25, where the edge amount 101 is calculated.

FIG. 4 is a block diagram of an example of the maximum value detection circuit and the minimum value detection circuit. The image data 102, 103, 104 of the line selected by the selector 22 is the latches 30a to 30c, 31a to 31c, 32a.
Each pixel is delayed by 32c.

The comparator / selector 33a compares the outputs of the latches 31a and 32a, which means the data comparison between a pixel and the pixel immediately preceding it. Similarly, the comparator 34a compares the output result of 33a with the data of the pixel two ahead. Therefore, the output of 34a becomes the maximum value or the minimum value of three consecutive pixels in one line.

FIG. 5 shows a configuration example of the comparator selector in the maximum value detector 23. The inputs A and B are output to the comparator 40 and the latches 41 and 42, respectively. Here, in the comparator 40, A> B
If the output is set to "1" at the time of, when the data is A> B, the output of the output of the comparator 40 becomes "1", and this signal passes through the inverter 43 and the latch 41. Enter the enable terminal of. If latches 41 and 42 are negative logic, output 4
5 is the value of A. Conversely, when A ≦ B, the output 45 has the value of B. As a result, the larger value of A and B is output to 45.

On the other hand, the minimum value detector 24 can be easily realized by inserting the inverter 43 in the latch 42 side.

Next, the comparison selector 35 detects the maximum value or the minimum value of the first line and the second line, and the comparison detector 36 detects this result and the maximum value or the minimum value of the third line.

As a result, the output of the comparison selector 36 becomes the maximum value or the minimum value in the 3 × 3 pixel block.

FIG. 6 is a block diagram of another embodiment for obtaining the edge detection output 101. In the data of three lines from the selector 22, the central pixel is 50c and the peripheral pixels 50a, 5
The values are 0b, 50d, and 50c. The central pixel is multiplied by a constant which is the multiplier 51, and the result is input to the subtractor 33. On the other hand, the sum of peripheral pixels is calculated by the adder 52 and the sum is input to the subtractor 33.

As a result, the output of the subtractor 53 is output = constant × (50c)-{(50a) + (50b) + (50d) + (50e). This corresponds to the Laplacian calculation shown in FIG. 7 (a) (however, constant = 5). The Laplacian calculation is not limited to this, and the ones shown in FIGS. 7B and 7C are also conceivable.

FIG. 9 is a block diagram of an example in which this embodiment is applied to a color image. A color image input device 90 outputs a red signal, a green signal, and a B1ue signal separated into three colors. These signals are converted into 8-bit digital signals for each color by the A / D converter 91. The correction circuit 92 performs shading correction, complementary color conversion from RGB signals to YMC signals, and masking processing, and outputs Yellow signals, Magenta signals, and Cyan signals.

The three color signals are input to the error correction circuit 93. The error correction circuit 93, the binarization circuit 94, and the error calculation circuit 95 can be realized by having the above-mentioned circuits for three colors.

As explained above, by changing the error distribution ratio according to the edge amount, the edge part can be reproduced sharply and the granular noise in the uniform density part (shear part / highlight part) can be reduced. I was able to. In addition, it was possible to suppress moire in the halftone dot image.

As described above, according to the present invention, when the error data generated by the binarization process is weighted and distributed to a plurality of pixels based on the weighting coefficient, the edge component of the image is detected. By changing the weighting ratio of the weighting factor for a plurality of images according to the detection result, it is possible to sharply reproduce the edge portion of the image and prevent the occurrence of stripe patterns and graininess noise.

[Brief description of drawings]

FIG. 1 is a block configuration diagram of the image processing apparatus of this embodiment, FIG. 2 is a block configuration diagram showing an embodiment of an error correction circuit, and FIG. 3 is a block configuration diagram showing an embodiment of an edge detection circuit. FIG. 7 is a block configuration diagram of the maximum value or minimum value detection circuit, FIG. 5 is a block configuration diagram of the comparison selector, FIG. 6 is a block configuration diagram of another embodiment of the edge detection circuit, and FIG. (C) is a diagram showing an example of a Laplacian operator, FIG. 8 is a diagram showing an example of a weighting function, FIG. 9 is a block configuration diagram showing an embodiment of a color image forming apparatus, and FIG. 10 is a target pixel and surroundings. It is a figure which shows the example of 4 pixels. In the figure, 1 ... Input sensor section, 2 ... A / D converter, 3 ...
Correction circuit, 4 ... Error correction circuit, 5 ... Binarization circuit, 6
... error calculation circuit, 7 ... printer, 8 ... edge detection circuit, 9 ... adder, 10 ... weighting generator, 10a to 10d
... Lookup table, 11 ... line buffer.

Claims (1)

[Claims] 1. Input means for inputting image data, binarizing means for binarizing the input image data into binary data, and error data generated by the binarizing processing in the binarizing means. And a distribution unit that distributes the error data obtained by the calculation unit to the image data of a plurality of pixels that has not been binarized by weighting them based on weighting factors. An image processing apparatus, comprising: a detection unit for detecting, wherein the distribution unit changes a weight ratio of the weight coefficient with respect to the plurality of pixels according to a detection result of the detection unit.