JPH11346315A - Image processing unit and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce starvation while suppressing apparent fog of an edge part. SOLUTION: A smoothing section 2 smoothes a received image signal and outputs a mean value signal (f). Furthermore, a weight coefficient decision section 3 obtains a contrast weight ww in response to a contrast (h) and a correction object degree wh corresponding to a target pixel value (g) to calculate a weight coefficient W. Moreover, an edge correction coefficient decision section 4 discriminates whether or not an edge has a prescribed contrast and a coefficient decision section 23 decides an edge correction coefficient ew. Then a correction pixel value calculation section 5 uses the mean value signal (f) based on the weight coefficient W to correct the target pixel value (g) thereby reducing starvation and decreases the density furthermore by the edge correction coefficient ew for a very small area with low density in the vicinity of the edge thereby emphasizing the edge.

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 an image processing method that can be used when forming an image in an image forming apparatus such as a digital copying machine or a printer.

[0002]

2. Description of the Related Art As one of developing methods in a conventional image forming apparatus, there is a two-component magnetic developing method using an insulating toner and magnetic particles, and is widely used. In this two-component magnetic development method, when trying to reproduce an image in which a high-density part and a low-density part of the same color are adjacent to each other, white spots (hereinafter referred to as starvation) occur due to the output reproduction characteristics of the two-component magnetic development method. It is known to occur. This phenomenon occurs when the toner adhered on the photosensitive drum is pulled back into the developer layer of the developing device.

In order to solve such a problem, there is a method described in, for example, JP-A-5-281790. In this method, the generation of starvation is prevented by improving the developing section, such as by limiting the particle diameters of the insulating toner and the magnetic particles and increasing the frequency of the bias voltage. However, such a limitation in the developing unit involves an increase in the size and cost of the apparatus, and in some cases, it is difficult to achieve compatibility with other requirements, such as a higher resolution.

As another solution, for example, Japanese Patent Application Laid-Open
There is a method described in US Pat. In this method, an edge portion where the density changes is detected, and a correction amount is determined based on an amount of toner pulled back from a photosensitive drum into a developer layer of a developing device, and a low density portion to be corrected is determined. Correct the image signal. By forming an image using the corrected image signal, occurrence of starvation is prevented.

On the other hand, if an attempt is made to reproduce an image in which a high-density portion and a low-density portion of the same color are adjacent to each other, the density of the high-density minute area in the adjacent portion (edge portion) is increased. It is known that the reproducibility of the edge portion is improved by reproducing the density of the minute area on the low density side thinner. FIG. 8 is an explanatory diagram of an example of an image signal including an edge and a density distribution of a formed image. For example, consider a case where an image signal including an edge where a low density portion and a high density portion are adjacent to each other as shown in FIG. At this time, as shown in FIG. 8B, the density of a minute area of about 1 to several pixels on the low density side adjacent to the edge is further reduced,
Further, by reproducing the density of a minute area of about one to several pixels on the high density side adjacent to the edge more densely, the edge can be apparently reproduced sharply. In the electrophotographic method, such characteristics can be obtained when an image is formed.

However, such a decrease in density on the low-density side acts as edge enhancement as long as it occurs in a minute area. However, the above-described starvation occurs in a wide range of several tens of pixels or more, and the density decreases. A drop is visible. FIG. 8C shows the density distribution of the image in which the starvation has occurred. To address this,
For example, the method described in Japanese Patent Application No. FN96-00432 described above can be used. With this method, the image signal shown in FIG. 8A is corrected to the image signal shown in FIG. By forming an image using the image signal shown in FIG. 8D, the density distribution of the formed image becomes as shown in FIG. 8E, and starvation can be prevented.

In the density distribution shown in FIG. 8 (E), there is almost no reduction in density even in a small area on the low density side adjacent to the edge. Therefore, the sharpness of the edge decreases,
There was a problem that edges appeared blurred. This occurs because the density of the image signal on the low density side is increased at the edge portion as shown in FIG. If the amount of adjustment of the density is reduced, apparent blurring can be prevented to some extent, but on the contrary, the occurrence of starvation must be tolerated to some extent.

[0008]

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and aims to reduce starvation while suppressing apparent blurring of an edge portion, and to form an image capable of forming a high quality image. It is an object of the present invention to provide an image processing device and an image processing method for generating a signal.

[0009]

According to the present invention, a signal for smoothing an input image signal to reduce starvation is generated, and the input image signal is converted into an image signal based on the smoothed image signal. A weight coefficient indicating whether the degree is to be corrected is determined. By correcting the input image signal with the image signal smoothed according to the determined weight coefficient, starvation can be reduced.

However, since the edge portion is apparently blurred as described above, an edge correction coefficient for correcting the density of the minute portion on one side of the edge portion is determined, and the determined edge correction coefficient is also taken into consideration. By correcting the input image signal, the contrast of the edge portion is improved, and apparent blurring of the edge is suppressed.

In this way, the starvation can be reduced while suppressing the apparent blur at the edge portion, and a high-quality image can be formed.

[0012]

FIG. 1 is a block diagram showing an embodiment of an image processing apparatus according to the present invention. In the figure, 1 is an input buffer, 2 is a smoothing unit, 3 is a weight coefficient determining unit, 4 is an edge correction coefficient determining unit, 5 is a corrected pixel value calculating unit, 11 is a contrast calculating unit, 12 is a correction target selection table, 13
Is a weight coefficient selection table, 14 is a weight coefficient calculator, 21
Denotes an edge detection unit, 22 denotes an edge determination unit, and 23 denotes a coefficient determination unit.

An input buffer 1 receives an input image signal and holds an image signal for approximately a predetermined number of lines.
Then, the value of the pixel of interest and a pixel in a predetermined area including the periphery of the pixel of interest are output. Hereinafter, this predetermined area is called a window. The value of the target pixel is set as a target pixel value g. The size of the window is, for example, m ×
It can be a rectangular area of n pixels. As a specific example,
For example, when correcting 14 pixels in the main scanning direction and 7 pixels in the sub-scanning direction at the edge portion, the window size can be 29 × 15 pixels. Of course, the size and shape of the window are arbitrary, and may be any size and shape necessary for processing by the smoothing unit 2 and the edge detection unit 21. Further, for example, if the image signal is stored in a storage device such as an image memory and a pixel value of a necessary area can be read from the image memory or the like,
A configuration without the input buffer 1 is also possible.

The smoothing unit 2 performs a smoothing filtering process on the basis of the image signal in the window output from the input buffer 1, and outputs an average signal f at the pixel of interest. The average signal f is used to reduce starvation.

The weight coefficient determining section 3 determines a weight coefficient indicating how much the target pixel value g is corrected by the average value signal f output from the smoothing section 2. Weight coefficient determination unit 3
Is the contrast calculation unit 11, the correction target selection table 1
2, a weight coefficient selection table 13, a weight coefficient calculator 14, and the like.

The contrast calculating section 11 calculates a target pixel value g.
And the contrast h between the pixel and surrounding pixels are calculated. Here, the maximum density is searched from the image signals in the window output from the input buffer 1, and the density difference between the searched maximum density and the target pixel value g is calculated as the contrast h.

The correction target selection table 12 has a correction target degree wh indicating how much the target pixel needs correction.
Is a table for obtaining from the target pixel value g. FIG.
9 is a graph showing an example of a relationship between a target pixel value and a correction target degree set in a correction target selection table. For example, on a white background, even if a white spot occurs, it is not known. In addition, when the density is very high even on the side where the density is low at the edge, starvation does not occur because the contrast of the edge portion is small. In these cases, the necessity of correction for reducing starvation is low. As described above, the degree to which the starvation should be corrected differs depending on the density of the background (the side with the lower density at the edge). Therefore, as shown in FIG. 2, the correction target degree wh is reduced in the portion where the value of the target pixel value g is small and in the portion where the value is large, indicating that the necessity of correction is small. Further, since the correction is performed at the intermediate density, the correction target degree w
h is set to 1. Such a correction target degree wh is stored in the correction target selection table 12 in association with the target pixel value g. Accordingly, the correction target selection table 12 determines the degree of correction in accordance with the target pixel value g by the correction target degree wh.
Can be output as Correction target selection table 1
2 can be configured with a look-up table that inputs the target pixel value g and outputs the correction target degree wh, but is not limited thereto, and calculates the correction target degree wh by calculation, etc.
It can be realized with various configurations.

The weight coefficient selection table 13 is a table for calculating a contrast weight ww corresponding to the contrast h output from the contrast calculator 11.
The contrast weight ww indicates how much the target pixel value g is corrected by the average value signal f output from the smoothing unit 2. When the contrast h is large, the influence of the starvation increases, but the change of the average signal f also increases. By adjusting both, the starvation can be apparently reduced. If the correspondence between the change of the average signal f according to the contrast h and the change of the correction amount for reducing the starvation is previously obtained by an experiment, the average signal f can be used as the correction amount for reducing the starvation. The corresponding relationship for making them substantially coincide is obtained. The value indicating this correspondence is the contrast weight ww corresponding to the contrast h. FIG. 3 is a graph showing an example of the relationship between the contrast and the contrast weight set in the weight coefficient selection table. For example, a weighting coefficient ww as shown in FIG. 3 can be set in association with the contrast h obtained by the contrast calculator 11. The weight coefficient selection table 13 receives the contrast h output from the contrast calculator 11 as an input,
It can be configured as a look-up table that outputs the corresponding contrast weight ww, but is not limited to this, and can be implemented in various configurations such as a configuration in which the contrast weight ww is obtained by calculation.

The weight coefficient calculating section 14 calculates a weight coefficient W from the correction target degree wh and the contrast weight ww.
This weighting factor W takes into account the degree of correction wh, which indicates how much correction is necessary, to the contrast weight ww, which indicates how much the target pixel value g is corrected by the average value signal f according to the contrast h. It indicates how much correction is to be performed on the input target pixel value g by the average value signal f. Specifically, the weight coefficient W is obtained by multiplying the contrast weight ww and the correction target degree wh (W =
ww × wh). Of course, the weight coefficient W may be obtained by another method such as using another calculation method or using a lookup table. In addition, since the starvation occurs on the side of the edge portion where the density is low, it is not necessary to perform correction on the side where the density is high. for that reason,
When the contrast h is 0 (the target pixel value g is the maximum density) or the target pixel value g is larger than the average value signal f, the target pixel is not an edge part or the density of the edge part is higher. Pixel. In such a case, since there is no need to perform correction, the weight coefficient calculating unit 14
, A weighting coefficient for which no correction is made, specifically, 0 is compulsorily set.

The edge correction coefficient determination section 4 determines an edge correction coefficient ew for correcting the density of a minute portion on the low density side in the edge portion of the input image signal to emphasize the edge. The edge correction coefficient determination unit 4 includes an edge detection unit 21, an edge determination unit 22, a coefficient determination unit 23, and the like.

The edge detecting section 21 receives a pixel in a predetermined area including a pixel of interest from the input buffer 1 and detects the presence of an edge. The edge detection unit 21 does not need to refer to all the pixels in the window output from the input buffer 1, but refers to a pixel of a size capable of detecting an edge, specifically, for example, an area of about 3 × 3 pixels. do it.

The edge determination section 22 determines whether or not the edge is detected by the edge detection section 21 and whether or not the contrast h calculated by the contrast calculation section 11 of the weight coefficient determination section 3 is larger than a predetermined threshold value CNTLMT. Is determined. When the edge is detected by the edge detection unit 21 and the contrast h is larger than the threshold CNTLMT, it is determined that the edge is to be subjected to the correction processing. The determination result and the contrast h are sent to the coefficient determination unit 23.

The coefficient determining unit 23 sets the edge correction coefficient ew to a value corresponding to the contrast h when it is determined according to the determination result of the edge determining unit 22 that the edge should be subjected to the correction processing. FIG. 4 is a graph showing an example of the relationship between the contrast and the edge correction coefficient ew set in the coefficient determination unit 23. Here, the edge correction coefficient ew is a value that does not perform edge correction when it is 1 and that only has a correction amount for preventing starvation when it is 0. As shown in FIG. 4, when the contrast h is small, the correction amount for preventing starvation is also small, so that the edge correction amount is reduced. Conversely, when the contrast h is large, the correction amount for preventing starvation is also large, so the edge correction amount is large.
Although FIG. 4 shows that the edge correction coefficient ew decreases linearly with respect to the contrast h, it can be actually obtained experimentally. The determination of the edge correction coefficient ew according to the contrast h can be performed using, for example, a look-up table. of course,
Other methods may be applied, for example, as long as they have a linear relationship as shown in FIG. 4, they can be obtained by calculation based on a simple relational expression.

The coefficient determining section 23 includes an edge determining section 22
If it is determined that the edge is not an edge to be subjected to the correction processing, the edge correction coefficient ew is set so that the correction processing is not performed on the minute portion near the edge. For example, the edge correction coefficient ew is set to 1.

The correction pixel value calculation section 5 includes a weight coefficient determination section 3
The target pixel value g is corrected by the average value signal f output from the smoothing unit 2 in accordance with the weight coefficient W determined in the step S1 and the edge correction coefficient ew determined in the edge correction coefficient determination unit 4 to obtain the output image signal G. Generate and output. Output image signal G
Can be calculated according to the following equation, for example. G = (f × W) + (1−W) × ew × g The output image signal G calculated by this equation is a pixel of interest in an area near the edge where starvation occurs, depending on the ratio of the weighting coefficient W. The value g will be corrected by the average value signal f. Thereby, starvation can be reduced. Further, in the minute area near the edge, the target pixel value g is limited by the edge correction coefficient ew, so that the density of the minute part on the lighter side near the edge can be reduced and the edge can be emphasized. In this way, the output image signal G has reduced starvation while suppressing the apparent blur at the edges.

FIG. 5 is a flowchart showing an example of the operation of the image processing apparatus according to the embodiment of the present invention.
Various parameters are set before an image signal is input. For example, the smoothing filter parameters of the smoothing unit 2, the values of the correction target selection table 12 and the weight coefficient selection table 13 of the weight coefficient determination unit 3, and the edge correction coefficient determination unit 4
The value of the threshold CNTLMT in the edge determination unit 22 is set in advance.

When an image signal is input, it is sequentially stored in the input buffer 1 in S31. After an image signal of a predetermined number of pixels or a predetermined number of lines is input,
Pixels in a predetermined window including the target pixel are cut out and output while sequentially moving the target pixel. In response, the smoothing unit 2, the weight coefficient determination unit 3, and the edge correction coefficient determination unit 4 start processing. Of course, each unit need not use all the pixels in the window output from the input buffer 1. Here, as an example, the smoothing unit 2 and the weight coefficient determining unit 3 use all the pixels in the window, and the edge correction coefficient determining unit 4 uses only the pixels in the area including the target pixel smaller than the predetermined window. It shall be.

The smoothing unit 2 performs a smoothing filter operation in S32 based on the value of each pixel in the window to obtain an average signal f corresponding to the pixel of interest.

In the weight coefficient determining section 3, first, a contrast h is calculated by the contrast calculating section 11 based on the value of each pixel in the window. For that purpose, the maximum value is searched from each pixel value in the window in S33. The searched maximum value is set to MAXC. Further, in S34, a difference between the maximum value MAXC and the target pixel value g is calculated, and this is set as a contrast h. The contrast h and the maximum value MAXC are also sent to the edge determination unit 22 of the edge correction coefficient determination unit 4.

The weight coefficient calculator 14 receives the contrast h calculated by the contrast calculator 11, and determines in step S35 whether or not the contrast h is 0 or less. If the contrast h is equal to or less than 0, it indicates that the window has a uniform density, or that the target pixel is a pixel with a higher density at the edge. Similarly, the target pixel value g
Is greater than or equal to the average signal f, it indicates that the target pixel is a pixel on the higher density side. Since the starvation occurs on the side where the density of the edge portion is low, it is not necessary to perform the correction process on the side where the density of the edge portion is high. Of course, it is not necessary to perform the correction process on the flat portion. Therefore, the weight coefficient W is set to 0 in S36.
To prevent the correction by the weight coefficient W from being performed.

When the contrast h is larger than 0 and the target pixel value g is smaller than the average signal f in S35, the target pixel is a pixel on the edge portion where the density is lower. Therefore, starvation may occur. First, in S37, a contrast weight ww for correcting the target pixel value g is obtained from the weight coefficient selection table 13 based on the contrast h. At the same time S38
, The correction target degree wh is obtained from the correction target selection table 12 based on the target pixel value g. Further, in S39, the weighting factor calculation unit 14 calculates the weighting factor W from the contrast weight ww obtained from the weighting factor selection table 13 and the correction target degree wh obtained from the correction target selection table 12.

On the other hand, the edge detection section 21 of the edge correction coefficient determination section 4 searches the maximum value in S40 from a small area including the target pixel in the input buffer 1, for example, an area of about 3 × 3 pixels. This maximum value is defined as MAXE. Here, the processing of the edge detection unit 21 is limited to the search for the maximum value MAXE.
Perform in. Of course, the edge detection unit 21 may determine whether an edge is present or not by various known methods.

In step S 41, the edge determination unit 22 compares the maximum value MAXE obtained by the edge detection unit 21 with the maximum value MAXC obtained by the contrast calculation unit 11. As described above, since the maximum value MAXC is the maximum value in the window, the maximum value M is set in an area smaller than the window.
The presence of a pixel having the same value as AXC indicates that the small area for detecting the edge includes at least the pixel on the higher density side. Maximum value MAXC and maximum value MAXE
Are equal to each other, it is further determined in S42 whether or not the contrast h is larger than a preset threshold CNTLMT. Contrast h is threshold CNT
If it is greater than LMT, the threshold C within a given window
It can be seen that there is an edge having a density difference equal to or higher than NTLMT, and that the pixel of interest is a pixel with a lower edge density. From the determinations in S41 and S42, it is determined that a high-density portion exists in the small region, and that the target pixel is a pixel on the lighter side. That is, the target pixel is a pixel on the thin side near the edge. The pixel determined in this way is a pixel to be corrected for edge enhancement. Therefore, in order to emphasize the edge, S43
, The coefficient determination unit 23 calculates an edge correction coefficient ew based on the contrast h. In S41, the maximum value M
If AXC is not equal to the maximum value MAX, and S
If the contrast h is equal to or smaller than the threshold value CNTLMT in S42, the edge determination coefficient ew is set to 1 in the coefficient determination unit 23 in S44, so that the edge enhancement is not corrected.

After the average value signal f, the weight coefficient W, and the edge correction coefficient ew are obtained in this way, the corrected pixel value calculator 5 calculates the output pixel signal G in S45. The calculation of the output pixel signal G can be obtained by calculation according to the above-described calculation formula. Of course, other formulas,
It may be obtained by another method such as using a multidimensional lookup table. The output pixel signal G calculated in this way may be output as a correction processing result.

Next, an example of a correction processing operation in an embodiment of the image processing apparatus of the present invention will be described using a specific example. FIG. 6 is a diagram illustrating an example of an input image signal, and FIG. 7 is a diagram illustrating an example of an output image signal after a correction process. Although the image actually has a two-dimensional spread, only a part of a certain line is shown here. Here, as an example, as shown in FIG. 6, an image in which a density difference exists between the pixel position B and the next pixel and a step-like edge exists is assumed. This density difference is the contrast h.

Assuming that the width of the window cut out by the input buffer 1 is the illustrated width, the edge of the pixel position B is not included in the window on the left side of the pixel position A and on the right side of the pixel position C. Therefore, the pixels from pixel position A to pixel position C will be described here. When the pixels from the pixel position A to the pixel position C are set as the target pixel, since the window includes pixels having different densities, the smoothing unit 2
By performing the smoothing filter processing by FIG.
Output an average signal f indicated by a broken line. When the pixel from pixel position A to pixel position B is the pixel of interest, the contrast calculation unit 11 outputs the density difference of the edge portion as the contrast h. It becomes 0.

The weight coefficient selection table 13 receives the contrast h from the contrast calculator 11 and outputs a corresponding contrast weight ww for each pixel. On the other hand, from the correction target selection table 12, it is assumed that any pixel from the pixel position A to the pixel position C is to be corrected and the correction target degree wh = 1 is output.

In the weight coefficient calculating section 14, the contrast h
Is determined, and for each pixel from the pixel position A to the pixel position B, the weight coefficient W is calculated from the correction target degree wh and the contrast weight ww. Here, since the correction target degree wh = 1, the weight coefficient W = the contrast weight ww. Further, the right side from the pixel on the right side of the pixel position B is a pixel on the side where the density of the edge portion is higher, and the contrast h = 0 and the pixel value of interest g> the average value signal f. Therefore, the weight coefficient W = 0. .

Further, in the edge detecting section 21, the pixel position B
When the pixel on the left side is the target pixel, a pixel having a high density is not detected in a detection area smaller than the window, for example, a 3 × 3 pixel. Therefore, the edge determination unit 22 does not determine each pixel on the left side of the pixel position B as an edge and outputs 1 as the edge correction coefficient ew.

As a result, the pixel positions A to B
Since the edge correction coefficient ew = 1 for each pixel up to the pixel to the left of the pixel, a correction process based on the weight coefficient W is performed, and as shown in FIG. Become. By thus increasing the density toward the edge, starvation can be suppressed.

For the pixel position B, the edge detector 21
, A portion having a high density is detected, and it is determined that the contrast h is sufficiently large, and the edge determination unit 22 determines that the pixel position B is a pixel to be subjected to edge correction. The coefficient determination unit 23 calculates an edge correction coefficient ew corresponding to the contrast h, and outputs the edge correction coefficient ew to the correction pixel value calculation unit 5. As a result, the correction pixel value calculation unit 5 performs a correction process using the edge correction coefficient ew and the weight coefficient W, and the target pixel value g is reduced by an amount corresponding to the edge correction coefficient ew, and as shown in FIG. Thin pixels are generated.

On the right side of the pixel on the right side of the pixel position B, the contrast h becomes 0, so that the edge correction coefficient e
Since w = 1 and the weight coefficient W also becomes 0, the corrected pixel value calculation unit 5 outputs the target pixel value g as it is to the output image signal G
Will be output as

In this way, the input image signal as shown in FIG. 6 is obtained for each pixel from pixel position A to pixel position B on the low density side of the edge portion as shown in FIG.
In order to suppress the starvation, a process of increasing the density is performed, and a process of reducing the density at the pixel position B to emphasize the edge is performed. By forming an image using the output image signal G that has been subjected to the correction processing in this way, starvation can be reduced without apparently blurring the edge portion. The density distribution of the formed image has, for example, characteristics close to the distribution shown in FIG.

In the above specific example, an example of an edge transitioning from a low-density portion to a high-density portion is shown, but an edge transitioning from a high-density portion to a low-density portion is similarly corrected to the low-density side of the edge portion. Can be applied. In the above-described specific example, the two-dimensional window is set, but each process is performed. Therefore, in both the main scanning direction and the sub-scanning direction, the low density side of the edge portion is used. The above-described correction processing can be performed. For example, when the region where the starvation occurs is different between the main scanning direction and the sub-scanning direction, the width of the window in the main scanning direction and the width in the sub-scanning direction may be set according to the region where the starvation occurs. Also, even in the same direction, even when the region of occurrence of starvation is different between when shifting from a low density portion to a high density portion and when shifting from a high density portion to a low density portion,
This can be dealt with by appropriately setting the left and right and top and bottom widths of the target pixel in the window. Of course, as described above, the shape of the window is not limited to a rectangle, but may be any shape such as a circle or a diamond, and may be any shape and size effective for preventing starvation.

In the above description, the processing performed on a single-color image has been described. For example, when a color image is formed using a plurality of color materials at the time of image formation, each color image corresponding to each color material is formed. Then, the above-described processing may be performed.

[0046]

As is apparent from the above description, according to the present invention, starvation can be prevented while suppressing apparent blurring of an edge portion, and an image signal capable of forming a high quality image. Can be generated.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating an embodiment of an image processing apparatus according to the present invention.

FIG. 2 is a graph illustrating an example of a relationship between a target pixel value and a correction target degree set in a correction target selection table.

FIG. 3 is a graph showing an example of a relationship between contrast and contrast weight set in a weight coefficient selection table.

FIG. 4 is a graph showing an example of a relationship between a contrast and an edge correction coefficient ew set in a coefficient determination unit 23.

FIG. 5 is a flowchart illustrating an example of an operation of the image processing apparatus according to the embodiment of the present invention.

FIG. 6 is an explanatory diagram of an example of an input image signal.

FIG. 7 is an explanatory diagram of an example of an output image signal after a correction process.

FIG. 8 is an explanatory diagram of an example of an image signal including an edge and a density distribution of a formed image.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Input buffer, 2 ... Smoothing part, 3 ... Weight coefficient determination part, 4 ... Edge correction coefficient determination part, 5 ... Correction pixel value calculation part, 11 ... Contrast calculation part, 12 ... Correction object selection table, 13 ... Weight Coefficient selection table, 14 ... weight coefficient calculator, 21 ... edge detector, 22 ... edge determiner, 2
3. Coefficient determination unit.

Claims (5)

[Claims] 1. A smoothing means for smoothing an input image signal, and a weight indicating a degree of correction when correcting the input image signal using the image signal smoothed by the smoothing means. Weight coefficient determining means for determining a coefficient, and edge correction coefficient determining means for determining an edge correction coefficient for correcting the density of a minute portion on one side of an edge portion in an image with reference to the input image signal; The input image signal based on the smoothed image signal output from the smoothing means, the weight coefficient determined by the weight coefficient determining means, and the edge correction coefficient determined by the edge correction coefficient determining means. An image processing apparatus, comprising: a correction unit that corrects the image. 2. The image processing apparatus according to claim 1, wherein the weighting factor determining unit determines a correction target weight according to the input image signal, a contrast calculating unit that calculates a contrast with surrounding image signals, A contrast weight determining means for determining a contrast weight according to the contrast calculated by the contrast calculating means; and a correction weight determined by the correction weight determining means and the contrast weight determined by the contrast weight determining means. The image processing apparatus according to claim 1, further comprising a weight coefficient calculation unit configured to calculate the weight coefficient. 3. The weighting factor determining means according to claim 1, wherein said weighting factor determining means determines that the input image signal is output as it is in a density area on a side opposite to a density to be corrected by the edge correction coefficient determining means. The image processing apparatus according to claim 1, wherein the image processing apparatus determines: 4. The edge correction coefficient determining means includes: an edge detecting means for detecting an edge from the input image signal; and an edge for determining whether or not the edge detected by the edge detecting means is an edge to be corrected. The image according to any one of claims 1 to 3, further comprising: a determination unit; and a coefficient determination unit that determines the edge correction coefficient according to a determination result of the edge determination unit. Processing equipment. 5. A method for smoothing an input image signal and determining a weighting factor indicating a degree of correcting the input image signal using the smoothed image signal from the input image signal; Further, an edge correction coefficient for correcting the density of a minute portion on one side of the edge portion in the image with reference to the input image signal is determined, and the smoothed image signal, the weight coefficient, and the An image processing method, wherein the input image signal is corrected based on an edge correction coefficient.