JPH08107499A - Image processing unit - Google Patents

Abstract

PURPOSE: To obtain an image processing unit in which an intermediate tone image is binarized with the error spread method after edge emphasis processing and image data of pseudo intermediate tone hose threshold level used for binarization depends on the density of each picture element are generated. CONSTITUTION: An image processing circuit 1 is made up of an original image storage device 3 storing part of intermediate tone original image information, a RAM 5 storing each calculation result, a ROM 7 storing a weight coefficient matrix, an edge emphasis coefficient and a reference threshold level or the like, a CPU 9 applying various image processing to an image, and an output image storage device 11 storing data after error spread processing. Intermediate tone image data are stored in the original image storage device 3 via a bus 13 and the CPU 9 uses a program and data in the ROM 7 to conduct edge emphasis processing. Then a correction input density and a threshold level are calculated in the error spread processing and binarization processing is conducted.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, and more particularly, to a digital electrophotographic copying machine having a function of binarizing a halftone image to generate pseudo halftone image data. The present invention relates to an image processing apparatus in a machine, a thermal transfer type or an inkjet type printer, or the like.

[0002]

2. Description of the Related Art Conventionally, as a means for binarizing a halftone image to create a pseudo halftone image, a systematic dither method for binarizing the image according to a threshold matrix (dither matrix) table, etc. The various dither methods are widely used. However, these conventional methods have a contradiction that the matrix table needs to be large in order to improve the gradation reproducibility, and the matrix table needs to be small in order to obtain high resolution. It was difficult to achieve both reproducibility and high resolution.

In addition to the above, there is an error diffusion method as a method for achieving both gradation reproducibility and high resolution, which is given a relatively good evaluation among various conventional methods. The error diffusion method is a method of distributing an error between an actual density and a binarized value, which occurs when binarizing a halftone pixel, to peripheral pixels, correcting the density, and then correcting the density. Is determined by a predetermined threshold value and is determined as one of binary values.

However, in such an error diffusion method, in the edge portion where the density changes abruptly, the correction is not reflected until the distributed error reaches a constant value, and the edge portion where the density changes from high density to low density. In the above, the problem is that the recorded pixels in the low density area are biased in the direction in which the error is diffused, and the non-recorded pixels in the high density area are biased in the error diffusion direction at the edge portion where the density changes from low density to high density was there.

In order to solve this problem, the applicant has already proposed in Japanese Patent Application No. 6-175311 that the above-mentioned predetermined threshold value is fixed, and based on the input density of each pixel. We propose a method to determine the threshold. According to this method, the problem that the pixels are biased in the direction in which the error is diffused at the edge portion is solved.

[0006]

However, it has been found that this method tends to reduce the sharpness of the image, which leads to a reduction in subjective evaluation. The present invention relates to an improvement in a method of determining a threshold value based on the input density of each pixel, and even in an edge portion where the density changes abruptly, the pixels are not biased in the error distribution direction, and the sharpness is sharp. It is an object of the present invention to provide an image processing apparatus capable of creating a more preferable pseudo halftone image in which the deterioration of the degree is suppressed.

[0007]

According to the first aspect of the present invention,
In an image processing device for binarizing a halftone image to create pseudo halftone image data, an edge enhancing means for correcting the density of each pixel by performing an edge enhancing process on the halftone image, and the edge enhancing means. The binarization processing means for binarizing the density of each pixel corrected by the error diffusion method by an error diffusion method and determining the threshold value used for the binarization according to the density of each pixel is provided. This is a characteristic image processing apparatus.

According to a second aspect of the present invention, the edge emphasizing means has a density I of each pixel and a density I of pixels on the left and right of each pixel.
Based on L and IR, the corrected concentration I ′ is calculated by the following equation.

[0009]

(Equation 3)

The image processing apparatus according to claim 1, characterized in that The invention according to claim 3 is the above 2
The binarization processing unit determines the threshold value used for binarizing the density of each pixel for each pixel according to the density of each pixel, and the sum of the binarization errors distributed from the surrounding pixels for each pixel. Output of each pixel by comparing the corrected input density calculated by the corrected input density calculation means with the threshold value determined by the threshold value changing means. A binarization error generated in each pixel is calculated from the output density determining means for determining the density, and the output density determined by the output density determining means and the corrected input density calculated by the corrected input density calculating means 2 The image processing apparatus according to claim 1 or 2, further comprising: a binarization error calculation unit and a binarization error distribution unit that weights the binarization error and distributes the binarization error to peripheral pixels.

According to a fourth aspect of the present invention, the threshold varying means calculates the threshold Tval based on the density I of each pixel by the following equation.

[0012]

[Equation 4]

The image processing apparatus according to claim 3, wherein n is the number of gradations of the original halftone image, and A is determined by an arbitrary coefficient satisfying 0 <A ≦ 1. In the invention according to claim 5, the coefficient A is set to 1 /
The image processing apparatus according to claim 4, wherein the number is 2.

[0014]

According to the image processing apparatus of the first aspect, the edge enhancing means corrects the density of each pixel by performing the edge enhancing process of the halftone image. afterwards,
The binarization processing unit binarizes the density of each pixel corrected by the edge enhancement unit by an error diffusion method using a threshold value determined according to the density of each pixel. in this way,
Since the edge part of the halftone image is emphasized in advance and binarized by the error diffusion method using the variable threshold value, the sharpness of the edge part is reduced and the edge emphasis is caused by the error diffusion method using the variable threshold value. The improvement in sharpness due to the treatment offsets,
It is possible to suppress a decrease in the sharpness of the edge portion. Moreover, even if the edge enhancement processing is performed, the pixels are not biased in the direction in which the error is diffused in the edge portion. Therefore, the image processing apparatus of the present invention creates a more preferable pseudo-halftone image in which the pixels are not biased in the error distribution direction even at the edge portion where the density changes abruptly, and the reduction in sharpness is suppressed. it can.

The edge emphasizing means calculates the corrected density I'based on the density I'of each pixel and the densities IL and IR of the pixels on the left and right of each pixel as follows.

[0016]

(Equation 5)

Can be configured to be determined by In addition, the densities IU, ID, IL
Based on IR, the corrected density I'may be obtained as in the following equation.

[0018]

(Equation 6)

Other than this, it is possible to use processing generally used in image processing as the edge enhancement processing. In the binarization processing means, for example, the threshold varying means determines a threshold value used for binarizing the density of each pixel for each pixel according to the density of each pixel, and the correction input density calculating means distributes from the peripheral pixels. The density of each pixel is corrected by the sum of the binarized errors thus obtained to obtain the corrected input density, and the output density determining means determines the corrected input density calculated by the corrected input density calculating means and the threshold value determined by the threshold varying means. And the output density of each pixel is determined, and the binarization error calculating means calculates the output density of each pixel from the output density determined by the output density determining means and the corrected input density calculated by the corrected input density calculating means. This is realized by calculating the binarization error that has occurred in step (1) and weighting the binarization error by the binarization error distribution means to distribute the binarization error to peripheral pixels.

The threshold varying means calculates the threshold Tval based on the density I of each pixel by the following equation:

[0021]

(Equation 7)

However, n may be determined by an arbitrary coefficient that satisfies the gradation number of the original halftone image A: 0 <A ≦ 1. The coefficient A is
For example, it is set to 1/2.

[0023]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing an image processing circuit 1 embodying an embodiment of the present invention. The image processing circuit 1 that performs the error diffusion processing in this embodiment is
It is configured as a computer and performs required arithmetic processing on input data. This image processing circuit 1
An original image storage device (image memory) 3 for storing a part of halftone original image information input by a digital electric signal, a RAM 5 for storing each calculation result, a ROM 7, and a RAM 5
CPU 9 for performing various image processings using an output image storage device (image memory) for storing data after error diffusion processing
11 and 11.

The ROM 7 stores an image processing program, a weighting coefficient matrix for distribution of binarization errors of peripheral pixels, an edge emphasis coefficient storing coefficient values used when performing edge emphasis processing, and a reference threshold value. Remembered Halftone image data of an original image input from an image input unit (not shown) such as an image pickup device or an external storage device that stores already captured image data is stored in the original image storage device 3 via the bus 13. It The original image data may be input pixel by pixel, line by line, or sent for one screen. The CPU 9 performs edge enhancement processing and error diffusion processing on the stored halftone image information using the image processing program in the ROM 7 and the data such as the matrix.

A procedure for determining the output value by calculating the corrected input density and the threshold value by the error diffusion processing after performing the edge enhancement processing will be described below with reference to FIGS. It should be noted that the number of gradations of the halftone original image data is 2 from 0 to 255.
It is assumed that there are 56 gradations. FIG. 2 is a flowchart showing the processing procedure of the binarization processing in this embodiment.

First, the input density Ix, y of the first pixel of interest is retrieved from the original image storage device 3 in which the original image data input by the image input means is stored (S10). Then, as shown in FIG. 3A, the input densities Ix, y of the target pixel and the densities Ix, y-1, Ix-1, y, Ix + of the four pixels vertically and horizontally adjacent to the target pixel are shown. Using the edge enhancement coefficient read from the ROM 7 based on 1, y, Ix, y + 1,
Edge enhancement processing is performed in accordance with Expression 1 to obtain the first corrected input density I'x, y (S20).

[0027]

(Equation 8)

"3" and "1/2" in the equation 1 are edge emphasis coefficients, which can be changed according to a desired degree of emphasis. Also, the densities Ix-1, y, Ix of the two pixels adjacent to the left and right
When it is based on + 1, y, it is expressed by Expression 2. In this case, "2" and "1/2" are edge emphasis coefficients.

[0029]

[Equation 9]

Next, the threshold value Tvalx, y for the target pixel is determined based on the first corrected input density I'x, y (S
30). In the present embodiment, the threshold value Tvalx,
y is determined.

[0031]

[Equation 10]

This 128 represents a half value of the number of gradations 256. For example, if the number of gradations is 1024, 512
Is set instead of 128. Further, a positive value of 1 or less may be used instead of 1/2 in Expression 3. Next, as shown in Equation 4,
The second correction input density I ″ x, y is calculated by adding the weighting error sum E of the binarization errors in the peripheral pixels distributed from the peripheral pixel to the target pixel to the first correction input density I′x, y. (S40).

[0033]

[Equation 11]

This error sum E is obtained in step S11, which will be described later, in the pixel processed before the current pixel of interest is processed.
It is a value that has already been obtained by accumulating in the binarization error storage buffer of each pixel due to the binarization error distribution of 0. Here, the threshold value Tvalx, y is compared with the second corrected input density I ″ x, y (S50), and the binary output density Ie is obtained.
The value of is determined (S60, S70). I ″ x, y ≧ Tva
In the case of lx, y, the output density Ie becomes "255" (S6
0) and I ″ x, y <Tval x, y, the output density Ie becomes “0” (S70). The output density Ie thus determined is stored in the output image storage device 11 (S8).
0).

Next, the binarization error e generated in the target pixel is calculated (S90). The binarization error e is obtained by subtracting the output density Ie from the second corrected input density I ″ x, y as shown in Expression 5.

[0036]

(Equation 12)

In order to distribute the binarization error e of the target pixel to the peripheral pixels, first, the weighting coefficient matrix α shown in Equation 6 is read from the ROM 7 (S100). Using this weighting coefficient matrix α, the binarization error generated in the target pixel is distributed to the peripheral pixels (S110). That is, the coefficient value having the positional relationship as shown in FIG. 3B with respect to the pixel of interest represented by “*” is obtained from the ratio of each element of the weighting coefficient matrix α and the sum of all elements, The binarization error of the pixel of interest is multiplied and added to the buffer for storing the binarization error of the pixel at the corresponding position.

[0038]

(Equation 13)

For example, for the pixel to the right of the pixel of interest, "7/48 (the element to the right of" * "in the matrix α of equation 6 / the sum of all elements)" is obtained as the weighting coefficient,
As a result, e × 7/48 is added to the pixel on the right side. In this way, the processing of the pixel at the position of the coordinates (x, y) is completed, and it is determined whether or not the next unprocessed pixel exists (S120). If there is, the pixel at the next coordinate is set as the pixel of interest. After being set (S130), the above-described processing is repeated.

The processing order of the pixels is that the leftmost pixel in the uppermost line is processed horizontally to the right, and the same processing is sequentially moved downward by one line to finally process the lower right pixel. Is the order. Then, if the above-described processing is executed for all the pixels, the binarization processing ends.

As described above, in the present embodiment, since the threshold value for binarization is determined according to the density of each pixel in the error diffusion method, even if the edge portion where the density changes abruptly, the error distribution of pixels is distributed. There is no bias in the direction. Furthermore, since the edge enhancement processing by the error diffusion method is executed, it is possible to suppress a decrease in sharpness and create a more preferable pseudo halftone image.

In the above embodiment, step S20 corresponds to the processing as the edge enhancing means, step S30 corresponds to the processing as the threshold varying means, step S40 corresponds to the processing as the corrected input density calculating means, and step S40 S50, S60, and S70 correspond to the process as the output density determining unit, step S90 corresponds to the process as the binarizing error calculating unit, and steps S100 and S110 correspond to the process as the binarizing error distributing unit. .

[Others] In the above embodiment, the edge enhancement processing (S20) and the error diffusion method (S30 to S110) were successively performed for each pixel. The processing (S20) is executed,
After that, the error diffusion method (S30 to S110) may be executed. Alternatively, the edge enhancement process (S20) may be performed by one device, and the data may be input to another device to perform the error diffusion method (S30 to S110).

[Brief description of drawings]

FIG. 1 is a block diagram illustrating an image processing circuit according to an embodiment.

FIG. 2 is a flowchart of the binarization process.

3A and 3B are explanatory diagrams showing a relationship between a target pixel and peripheral pixels, FIG. 3A is an explanatory diagram of edge enhancement processing, and FIG. 3B is an explanatory diagram of error distribution processing.

[Explanation of symbols]

1 ... Image processing circuit 3 ... Original image storage device 5 ...
RAM 7 ... ROM 9 ... CPU 11 ... Output image storage device 13 ... Bus

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI Technical display location G06F 15/68 320 A

Claims (5)

[Claims] 1. An image processing apparatus for binarizing a halftone image to create pseudo halftone image data, comprising: edge enhancing means for correcting the density of each pixel by performing edge enhancement processing of the halftone image. A binarization processing unit that binarizes the density of each pixel corrected by the edge enhancement unit by an error diffusion method and determines a threshold value used for the binarization according to the density of each pixel; An image processing apparatus comprising: 2. The edge emphasizing means calculates a corrected density I'based on the density I of each pixel and the densities IL and IR of the pixels on the left and right of each pixel by the following equation: ## EQU1 ## The image processing device according to claim 1, wherein the image processing device is determined by 3. The binarization processing means, threshold value varying means for determining a threshold value used for binarizing the density of each pixel for each pixel according to the density of each pixel, and 2 distributed from peripheral pixels. The correction input density calculation means for correcting the density of each pixel by the sum of the binarization error to obtain the correction input density, the correction input density calculated by the correction input density calculation means, and the threshold value determined by the threshold value varying means. 2 generated at each pixel from the output density determining means for comparing and determining the output density of each pixel, and the output density determined by the output density determining means and the corrected input density calculated by the corrected input density calculating means. 3. A binarization error calculation means for computing a binarization error, and a binarization error distribution means for weighting the binarization error and distributing the binarization error to peripheral pixels. Image processing Apparatus. 4. The threshold varying means calculates the threshold Tval based on the density I of each pixel by the following equation: However, the image processing apparatus according to claim 3, wherein: n is the number of gradations of the original halftone image, and A is determined by an arbitrary coefficient that satisfies 0 <A ≦ 1. 5. The image processing apparatus according to claim 4, wherein the coefficient A is 1/2.