JPH09270914A - Picture processing method and picture processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To execute an error diffusion processing and to form the picture of high quality, by operating pseudo-uniform random numbers (a) on the respective picture elements of a picture area whose density slope quantity of the input picture is larger than a prescribed threshold and operating a first positive value, a second negative value and a third positive value, based on the random numbers (a) on the respective picture elements of the picture area whose density slope quantity is not larger than the threshold. SOLUTION: An edge judgment signal 601 sets it to be '1', when the edge quantity of an input picture is larger than the threshold which is set and sets it to '0', when it is smaller. A pseudo-random number generation part 605 generates the pseudo-random number (a) is synchronizing with a picture element clock signal VESCK 602, and a pseudo-random number generation part 604 outputs data of +a/2, -a and +a/2 to the random numbers (a) generated in synchronism with VLCK which is divided into three frequencies in synchronism with VLSCK 602. An adder 607 adds a signal which a selector 606 selects with a judgment signal 601 to a video signal which is gradation-corrected. Thus, the picture elements which easily become the logic level are made to be regularly adjacent, and the rough surface feeling of the output picture can be suppressed.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method and an apparatus therefor, and more particularly, to an image processing method for processing image data so that high quality image formation can be performed by a FAX, a digital copying machine, a printer or the like. Regarding the device.

[0002]

2. Description of the Related Art Conventionally, in a digital copying machine, an original is illuminated with a halogen lamp or the like, and the reflected light is photoelectrically converted by using a charge coupled device such as a CCD and then converted into a digital signal to perform a predetermined process. After that, an image is formed using a recording device such as a laser printer or a liquid crystal printer.

Further, as an image forming method, a method of expressing each pixel of high resolution in multiple gradations has been adopted in recent years. For example, pulse width modulation, brightness modulation and the like. Also,
With the progress of the performance of each device that constitutes these recording devices, the development of higher resolution is being promoted. By the way, in an electrophotographic digital copying apparatus using a laser, if the resolution becomes higher,
The time for exposing the surface of the photoconductor becomes very short, and as shown in FIG. 1, a sufficient potential cannot be obtained on the surface of the photoconductor after exposure. As a result, the adhesion of the toner to the surface of the photoreceptor becomes extremely unstable with respect to the highlight portion (portion having a short exposure time) during development.

Therefore, the amount of toner adhered to the photosensitive member varies depending on the position of the pixel to be exposed, and as a result, uneven density occurs. Further, with the digitization and systematization of devices, the number of models that employ a binary output, which is a stable image forming method from the viewpoint of data handling and circuit scale, is increasing. Several methods such as the diffusion method have been proposed.

With respect to the dither method, the cycle of itself and the cycle of image data interfere with each other, resulting in image quality deterioration such as moire and its periodicity, resulting in poor reproduction of fine lines and characters. Therefore, as a higher quality image binarization method, an error diffusion method or the like has been proposed which binarizes while preserving the density. This method is excellent in reproducing thin lines, characters, etc. because it preserves the density and binarizes it, but since the pixels to which the error is distributed have directionality, a unique pattern is generated and the image quality is improved. It is said to be a factor of deterioration.

In order to solve such a problem, it has been proposed to add a pseudo random number or the like for generating a unique pattern to image data.

[0007]

However, by adding a pseudo-random number, it is possible to eliminate a peculiar pattern in a smooth portion where the density does not change so much, but the number of isolated points increases. , The toner is stable and hard to stick to the photoconductor. That is, in reproducing the halftone density,
The control was very difficult.

The present invention has been made in view of the above-mentioned conventional example, and an object thereof is to provide an image processing method and an apparatus therefor capable of forming a high quality image.

[0009]

In order to achieve the above object, an image processing method and apparatus according to the present invention have the following arrangement. That is, input means for inputting an image, and detection means for detecting a density gradient amount of the image input by the input means,
Pseudo-random number generating means for generating the pseudo-uniform random number a, and the pseudo-uniform random number generated by the pseudo-random number generating means for each pixel of the image region in which the density gradient amount detected by the detecting means is larger than a predetermined threshold value. a is applied to each pixel of the image area in which the density gradient amount detected by the detecting means is not larger than a predetermined threshold value, a first positive value based on the pseudo uniform random number a generated by the pseudo random number generating means. Value, a second negative value, and a third positive value, respectively, and a pseudo uniform random number operating means, and an error diffusion processing for performing an error diffusion processing based on the image operated by the pseudo uniform random number operating means. And means.

According to another aspect of the present invention, a density converting means for performing a predetermined density conversion on the image, a gradation converting means for gradation converting the density converted image by the density converting means into a binary pattern, The detecting means for detecting the density gradient amount of the image density-converted by the density converting means, the pseudo random number generating means for generating the pseudo uniform random number a, and the density gradient amount detected by the detecting means are larger than a predetermined threshold value. The pseudo uniform random number a generated by the pseudo random number generating means is applied to each pixel of the binary pattern obtained by the gradation converting means in the range corresponding to the image area, and the density detected by the detecting means is applied. Based on the pseudo uniform random number a generated by the pseudo random number generating means, each pixel of the binary pattern obtained by the gradation converting means in the range corresponding to the image area in which the gradient amount is not larger than the predetermined threshold is Positive value of 1, second negative Comprising values, and the pseudo uniform random number effectors exerting a third positive value, respectively, based on the working image in a pseudo uniform random effectors, and an error diffusion processing means for performing error diffusion processing.

Further, another invention is an input step of inputting an image, a detecting step of detecting a density gradient amount of the image input in the input step, and a pseudo random number generating step of generating a pseudo uniform random number a. , The density detected in the detection step is caused by causing the pseudo uniform random number a generated in the pseudo random number generation step to act on each pixel in the image region in which the density gradient amount detected in the detection step is larger than a predetermined threshold value. A first positive value, a second negative value, and a third positive value based on the pseudo-random number a generated in the pseudo-random number generation step are applied to each pixel in the image area whose gradient amount is not larger than a predetermined threshold value. A pseudo-uniform random number operation step for making each of the values of the above-mentioned values act, and an error diffusion processing step for performing error diffusion processing based on the image operated in the pseudo-uniform random number operation step.

According to another aspect of the present invention, there is provided a density conversion step of applying a predetermined density conversion to the image, a gradation conversion step of gradation converting the image density-converted in the density conversion step into a binary pattern, A detection step of detecting the density gradient amount of the image density-converted in the density conversion step, a pseudo random number generation step of generating a pseudo uniform random number a, and the density gradient amount detected in the detection step is larger than a predetermined threshold value. The pseudo uniform random number a generated in the pseudo random number generation step is applied to each pixel of the binary pattern obtained in the gradation conversion step in the range corresponding to the image area, and the density detected in the detection step is applied. Based on the pseudo uniform random number a generated in the pseudo random number generation step, each pixel of the binary pattern obtained in the gradation conversion step in the range corresponding to the image area in which the gradient amount is not larger than a predetermined threshold is Positive value of 1, second negative Comprising values, and the pseudo uniform random number effect step of reacting the third positive value, respectively, based on the working image in a pseudo uniform random action process, and an error diffusion processing step of performing error diffusion processing.

[0013]

DESCRIPTION OF THE PREFERRED EMBODIMENTS First, after summarizing the points of an image processing method and an image processing apparatus according to an embodiment of the present invention, a detailed description thereof will be given. The image processing method and the apparatus thereof according to the embodiment of the present invention vary the generation period of the pseudo random number added to the image signal according to the characteristic signal of the image data. Specifically, as shown in FIG. 2a, for a flat portion, that is, a pixel having a small edge amount, as shown in FIG. 2c,
The three density levels generated based on one pseudo-random number value are reflected in the image data of three pixels.

In this case, the pseudo random number value of a is converted into a pseudo random number value of + a / 2, -a, + a / 2, and then added to the corresponding three pixel data. this is,
This is because, as shown in FIG. 3, a pseudo random number value of + a / 2 is added to a pixel so that the pixel is likely to be at the logical level "1" during the error diffusion processing. That is, in the non-edge portion, it is possible to control so that the dots that are likely to be at the logic level "1" are not isolated but are gathered, and that the dots are scattered in the edge portion.

As a result, the density is preserved, and a good image quality is obtained without generating a peculiar pattern and with respect to a smooth portion such as a photograph and a portion where the density is abruptly changed such as an edge. Hereinafter, an image processing method and an image processing apparatus according to an embodiment of the present invention will be described in detail. First, the overall block configuration of the image processing apparatus of the present invention will be described with reference to FIG.

While exposing the original 200 with an exposure lamp (not shown), the exposure lamp (not shown) is moved in a direction perpendicular to the exposure lamp (hereinafter referred to as sub-scanning). Then, the reflected light is passed through the lens 201 and the image sensor 202
To image. Then, the analog signal processing unit 203 performs A / D conversion processing on the obtained analog image signal to digitize it. After that, the image processing unit 204 processes and processes the digital image signal, and the printer unit 205 forms an image.

The control of these series of operations is as follows.
The CPU circuit unit 210. The CPU circuit unit 210 includes a ROM 207 that stores a control program, a CPU 206 that executes the program, and a RAM 208. Here, detailed description of the CPU circuit unit 210, the analog signal processing unit 203, the printer unit 205, and the like will be omitted, and the image processing unit 204, which is a characteristic part of the present embodiment, will be described in detail.

FIG. 5 shows a specific configuration of the image processing section 204. <Shading correction circuit section> Analog signal processing section 20
The image signal converted into the digital signal in 3 enters the shading correction circuit 301. In the shading correction circuit 301, black correction and white correction processing is performed. With respect to the output from the CCD sensor, when the amount of input light is very small, the output level in the dark part becomes higher than the original level due to variations among pixels, flare, and the like.

Therefore, if the image is read as it is,
It is read brightly overall. Therefore,
The output level from the CCD when the exposure lamp is turned off is subtracted from the signal during image reading. Next, the white level correction will be described. In white level correction, a reference white plate placed at a reference position is read before scanning a document, and the sensitivity variations of the illumination system, optical system, and sensor are corrected based on the white plate data.

<Gradation Correction Unit> Next, the density conversion / gradation correction unit 302 will be described. Here, density conversion and gradation correction processing for correcting the gradation of the image output device are performed using an LUT (look-up table). First, log conversion is performed as density conversion processing for converting the read luminance signal into a density signal. log conversion table 3
The conversion value Dou corresponding to each input value Din set to 06
t is calculated from the following equation.

Dout = − (255 / Dmax) × log (Din / 255) The conversion value Dout corresponding to each input value Din obtained by this equation is set in the log conversion table in advance.
With this log conversion table 306, the input value Din is converted and output to the corresponding conversion value Dout. Then, this output is input to the gradation correction table 307.

Next, the gradation correction table 307 will be described. The gradation correction table 307 is for correcting the gradation characteristics of the image output device. An example of tone characteristics of an electrophotographic printer is shown in FIG. 6a. This shows the output printer density for the input data. FIG. 6b shows the input / output characteristics that should be set in the gradation correction table 307 in order to correct the nonlinear input / output characteristics to be linear.

In order to have the input / output characteristics shown in FIG. 6b, the gradation correction table 307 stores in advance the respective converted output values corresponding to the respective input data values. With this gradation correction table 307, the output value from the log conversion table 306 is input and linearly converted and output. After these correction processes are performed, the resulting image data is sent to the gradation conversion processing unit. The image signal that has undergone only density conversion is sent to the edge determination unit 303. This is because the characteristic correction on the printer side is a non-linear conversion, which adversely affects edge determination.

<Edge determination section> Next, the edge determination section 30
3 will be described with reference to FIG. Edge determination unit 30
3 is filters 505 and 50 for detecting the first derivative signal.
6. The filter is composed of, for example, a 5 × 5 pixel matrix as shown in FIG. 7.

In the filter 505, the signal of 5 lines (Video data) input in the sub-scanning direction is 1,
A process of obtaining the absolute value of the difference between the average value of the second line and the average value of the fourth and fifth lines is performed. Further, in the filter 506, signals of 5 lines (Videodat) input in the main scanning direction are input.
For a), the average value of the first and second pixels of each line,
A process of obtaining the average value of the fourth and fifth pixels and the absolute value of the difference is performed.

The image signal converted into a predetermined density value by the log conversion table 306 is a Fi for delay in the line direction.
After being delayed by a predetermined line in the Fo memories 501, 502, 503, and 504, the absolute value of the density difference is calculated by the density change detection filter 505 in the sub-scanning direction and the density change detection filter 506 in the main scanning direction. After that, the adder 507
At, each value is added. Then comparator 5
At 08, the value is compared with an arbitrary set value, and the value determined to be larger or smaller is output.

In the present embodiment, "1" is output as the edge detection signal when the edge amount is large, and "0" is output when the edge amount is small. Further, this arbitrary set value (threshold value) is set by the CPU 206 by the CPUBUS 30 prior to image reading.
5 is set in the register 509. <Gradation Conversion Processing Unit> Next, the gradation conversion processing unit 304 will be described with reference to FIG. Since the printer used in this embodiment is a binary printer, it is necessary to convert the input image signal into a binary signal. The gradation conversion processing unit 304 performs such processing.

The density-converted and gradation-corrected image signal in the gradation correction unit 302 is subjected to addition calculation in the adder 607 with a pseudo random number value with variable period, which is one of the features of this embodiment. After that, the error diffusion processing unit 608 performs binarization processing. The pseudo-random number added to this image data is
It is generated in synchronization with a pixel clock (hereinafter, VCLK) 602. The input VCLK 602 is the frequency division counter 60.
The frequency is divided by 3, and the pseudo random number generation unit 604 is entered.
In addition, the pseudo-random number generation unit 605 outputs the VCLK that is not divided.
602 inputs.

In the pseudo-random number generator 604, as shown in FIGS. 2a and 2c, the pseudo-random number value a generated in synchronization with the VCLK divided by 3 is + a / 2, -a, + a / 2. Data is output in synchronization with the input clock. This is because, as shown in FIG. 3, the image to which the value of “+ pseudo random number value / 2” is distributed is displayed in the error diffusion processing unit 6 in the subsequent stage.
This is because it is easy for the logic level to become "1" at 08. This is to suppress the roughness of the output image by regularly arranging pixels that are likely to be at the logic level “1” as close as possible.

Further, as shown in FIG. 2a, when the edge amount is larger than the set value (level1), the pseudo random number value is added to the image data as it is. FIG. 9 shows a timing chart in which the period of the pseudo-random number is variable according to the magnitude of the edge amount. The pseudo random number data of level 1 is a pseudo random number value generated from the pseudo random number generation unit 604, and
The pseudo random number of level 2 is a pseudo random number value generated by the pseudo random number generation unit 605.

The selector 606 is operated according to the edge determination signal.
Then, according to the edge signal, the level 2 is selected when the value is “0”, and the pseudo random number value of the level 1 is selected when the value is “1” and output to the adder 607 in the subsequent stage. As described above, the adder 607 adds the image data to the image data, binarizes it in the error diffusion processing unit 608, and sends the data to the printer unit 205 to form an image. (Second Embodiment) In the first embodiment (see FIG. 8), two pseudo random number generation units 604 and 605 are used, but in the second embodiment, one pseudo random number generation unit is used. 61
A configuration in which 0 is used is shown. This makes it possible to generate pseudo random numbers of two types of cycles corresponding to the first embodiment with a simpler configuration.

FIG. 10 shows the configuration of the gradation conversion processing unit according to the second embodiment. Here, the same processing units as those in FIG. 8 are given the same reference numerals. Therefore, here
Portions different from FIG. 8 will be described. In this embodiment, a process equivalent to the process of the two pseudo random number generation units 604 and 605 of FIG. 8 is configured by the pseudo random number generation unit 610 and the D flip-flop 611.

The structure of the pseudo random number generation unit 610 is the same as that of the pseudo random number generation unit 605 of FIG. 8, and generates and outputs a pseudo uniform random number in synchronization with the input VCLK. This output is
Input to the data input terminals of the selector 606 and the D-type flip-flop 611. The D flip-flop 611 latches the pseudo random number data output from the pseudo random number generation unit 610 in synchronization with the clock from the frequency division counter 603, and outputs the latched pseudo random number data to the selector 606.

The pseudo random number generating section in FIGS. 8 and 10 may be composed of a sequential circuit, or may be generated by executing a pseudo random number generating program including a microcomputer. The error diffusion processing unit may also be configured by a sequential circuit, or may be configured to include a microcomputer to execute the error diffusion processing program.

With this configuration, the gradation conversion processing unit can be configured with a simpler configuration. Even when the present invention is applied to a system composed of a plurality of devices such as a FAX, a digital copying machine, a printer, etc., an apparatus (FA
X, digital copying machine, printer, etc.).
Further, it goes without saying that the processing procedure of each unit in 203 and 204 represented by FIG. 4 described above is described by software, stored in the ROM 207 in advance, and executed by the CPU. .

An object of the present invention is to provide a storage medium storing a program code of software for realizing the functions of the above-described embodiments to a system or an apparatus, and to provide a computer (or CPU) of the system or apparatus.
And MPU) read and execute the program code stored in the storage medium.

In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention. As a storage medium for supplying the program code, for example, a floppy disk, a hard disk, an optical disk, a magneto-optical disk, C
A D-ROM, a CD-R, a magnetic tape, a non-volatile memory card, a ROM, etc. can be used.

When the computer executes the readout program code, not only the functions of the above-described embodiment are realized, but also the OS (Operating System) running on the computer based on the instruction of the program code. ) May perform some or all of the actual processing, and the processing may realize the functions of the above-described embodiments.

Further, after the program code read from the storage medium is written into a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, based on the instruction of the program code, It goes without saying that the CPU included in the function expansion board or the function expansion unit performs part or all of the actual processing, and the processing realizes the functions of the above-described embodiments.

When the present invention is applied to the above storage medium, the storage medium stores the program code corresponding to the above-described flowchart. As described above, by adding a pseudo-random number to the image data, the texture peculiar to the density preservation method is eliminated, and by changing the period of the random number, a stable halftone is obtained for a halftone image such as a photograph. It can be reproduced, and a sharp image can be provided for the edge portion.

[0041]

As described above, according to the present invention, a high quality image can be formed.

[Brief description of drawings]

FIG. 1 is a diagram showing a potential on a surface of a photoconductor.

FIG. 2a is a diagram showing binarization of an edge amount.

FIG. 2b is a diagram showing a pseudo random number period of level1.

FIG. 2c is a diagram showing a pseudo random number period of level2.

FIG. 2d is a diagram illustrating a rule for changing the type of pseudo-random number value applied to each pixel based on the edge amount.

FIG. 3 is a diagram illustrating an output image when a correction value based on a pseudo-random number having a 3-pixel cycle is applied to each pixel.

FIG. 4 is a configuration diagram of an image forming apparatus according to the present embodiment.

FIG. 5 is a configuration diagram of an image processing unit according to the present embodiment.

FIG. 6a is a diagram showing tone characteristics of a printer.

FIG. 6b is a diagram showing characteristics of a correction table.

FIG. 7 is a configuration diagram of an edge determination unit.

FIG. 8 is a configuration diagram of a gradation conversion processing unit according to the first embodiment.

FIG. 9 is a diagram showing operation timings of the gradation conversion processing unit according to the first embodiment.

FIG. 10 is a configuration diagram of a gradation conversion processing unit according to the second embodiment.

Claims (26)

[Claims] 1. An input unit for inputting an image, a detecting unit for detecting a density gradient amount of the image input by the input unit, a pseudo random number generating unit for generating a pseudo uniform random number a, and the detecting unit. The pseudo uniform random number a generated by the pseudo random number generation means is made to act on each pixel of the image region in which the detected density gradient amount is larger than a predetermined threshold value, and the density gradient amount detected by the detection means is predetermined. A first positive value, a second negative value, a third negative value based on the pseudo-uniform random number a generated by the pseudo-random number generation means, in each pixel of the image area not larger than the threshold value.
Pseudo-uniform random number acting means for respectively acting a positive value of, based on the image acted by the pseudo-uniform random number acting means,
An image processing apparatus comprising: an error diffusion processing unit that performs an error diffusion processing. 2. The detecting means applies first and second directional feature extraction filters corresponding to the main scanning direction and the sub-scanning direction to each partial image of the image input by the input means. The image processing apparatus according to claim 1, wherein the image density gradient amount of the image is detected. 3. The image processing apparatus according to claim 1, wherein the image input by the input unit is an image obtained by subjecting the original image to predetermined density conversion. 4. The image processing apparatus according to claim 3, wherein the predetermined density conversion is log conversion. 5. The first positive value, the second negative value, the third
The image processing apparatus according to claim 1, wherein the positive values of are + a / 2, -a, and + a / 2, respectively. 6. The image processing apparatus according to claim 1, wherein the action is addition. 7. The image processing apparatus according to claim 1, further comprising an image forming unit that forms an image based on the image obtained by the error diffusion processing unit. 8. A density conversion unit for performing a predetermined density conversion on an image, a gradation conversion unit for gradation-converting an image density-converted by the density conversion unit into a binary pattern, and a density conversion unit for density conversion. A detection unit for detecting the density gradient amount of the converted image, a pseudo random number generation unit for generating the pseudo uniform random number a, and an image region in which the density gradient amount detected by the detection unit is larger than a predetermined threshold value. The pseudo uniform random number a generated by the pseudo random number generation means is applied to each pixel of the binary pattern obtained by the gradation conversion means in the range, and the density gradient amount detected by the detection means is predetermined. 2 obtained by the gradation conversion means in the range corresponding to the image area not larger than the threshold value
Pseudo-uniform in which a first positive value, a second negative value, and a third positive value based on the pseudo-uniform random number a generated by the pseudo-random number generating means are applied to each pixel of the value pattern. Random number acting means, based on the image acted by the pseudo-uniform random number acting means,
An image processing apparatus comprising: an error diffusion processing unit that performs an error diffusion processing. 9. The detection means converts the first and second directional feature extraction filters corresponding to the main scanning direction and the sub-scanning direction into respective partial images of the image density-converted by the density conversion means. 9. The image processing apparatus according to claim 8, wherein the image processing apparatus operates to detect the amount of density gradient of the image. 10. The image processing apparatus according to claim 8, wherein the predetermined density conversion is log conversion. 11. The method according to claim 8, wherein the first positive value, the second negative value, and the third positive value are + a / 2, −a, and + a / 2, respectively. The image processing device described. 12. The image processing apparatus according to claim 8, wherein the action is addition. 13. The image processing apparatus according to claim 8, further comprising an image forming unit that forms an image based on the image obtained by the error diffusion processing unit. 14. An input step of inputting an image, a detection step of detecting a density gradient amount of the image input in the input step, a pseudo random number generation step of generating a pseudo uniform random number a, and a detection step of: The pseudo uniform random number a generated in the pseudo random number generation step is applied to each pixel of the image region in which the detected density gradient amount is larger than a predetermined threshold value, and the density gradient amount detected in the detection step is predetermined. A first positive value, a second negative value, a third negative value based on the pseudo-uniform random number a generated in the pseudo-random number generation step are assigned to each pixel in the image area not larger than the threshold value.
Pseudo-uniform random number operation step to apply a positive value of, respectively, based on the image operated in the pseudo-uniform random number operation step,
An image processing method comprising: an error diffusion processing step of performing an error diffusion processing. 15. The detection step applies first and second directional feature extraction filters corresponding to the main scanning direction and the sub-scanning direction to each partial image of the image input in the input step. 15. The image processing method according to claim 14, wherein the amount of density gradient of the image is detected. 16. The image processing method according to claim 14, wherein the image input in the input step is an image obtained by subjecting the original image to predetermined density conversion. 17. The image processing method according to claim 16, wherein the predetermined density conversion is log conversion. 18. The method according to claim 14, wherein the first positive value, the second negative value, and the third positive value are + a / 2, −a, and + a / 2, respectively. The described image processing method. 19. The image processing method according to claim 14, wherein the action is addition. 20. The image processing method according to claim 14, further comprising an image forming step of forming an image based on the image obtained in the error diffusion processing step. 21. A density conversion step of applying a predetermined density conversion to an image, a gradation conversion step of converting the density of the image converted in the density conversion step into a binary pattern, and a density conversion step of the density conversion step. A detection step of detecting the density gradient amount of the converted image, a pseudo random number generation step of generating a pseudo uniform random number a, and an image area in which the density gradient amount detected in the detection step is larger than a predetermined threshold value. The pseudo uniform random number a generated in the pseudo random number generation step is made to act on each pixel of the binary pattern obtained in the gradation conversion step of the range, and the density gradient amount detected in the detection step is predetermined. 2 obtained in the gradation conversion step in the range corresponding to the image area not larger than the threshold value
Pseudo-uniformity in which a first positive value, a second negative value, and a third positive value based on the pseudo-uniform random number a generated in the pseudo-random number generating step act on each pixel of the value pattern. Based on the image operated in the random number operation step and the pseudo uniform random number operation step,
An image processing method comprising: an error diffusion processing step of performing an error diffusion processing. 22. In the detecting step, first and second directional feature extraction filters corresponding to the main scanning direction and the sub-scanning direction are applied to each partial image of the image whose density is converted in the density converting step. 22. The image processing method according to claim 21, wherein the image processing method is operated to detect a density gradient amount of the image. 23. The image processing method according to claim 21, wherein the predetermined density conversion is log conversion. 24. The first positive value, the second negative value, and the third positive value are + a / 2, −a, and + a / 2, respectively. The described image processing method. 25. The image processing method according to claim 21, wherein the action is addition. 26. The image processing method according to claim 21, further comprising an image forming step of forming an image based on the image obtained in the error diffusion processing step.