JP3512581B2 - Image filtering method, image filtering device, and image edge enhancement processing device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image filtering method, an image filtering apparatus, and an image edge enhancement processing apparatus in image processing of a digital image obtained from a scanner, a digital camera, or other devices.

[0002]

2. Description of the Related Art Image filters conventionally used for image filtering include smoothing filters and sharpening filters. The smoothing filter is a filter for blurring the image by removing high frequency components of the image, and the sharpening filter is a filter for enhancing the contour of the image by enhancing the high frequency components of the image.

To enhance the contour of an image, there are a method using the smoothing filter and a method using the sharpening filter.

The method of using a sharpening filter is to filter an image using the filter, and the resulting image is a contour-enhanced image.

On the other hand, the method using the smoothing filter first generates a blurred image by filtering with the smoothing filter. Then, the difference between the blurred images is taken from the original image to generate a difference image. An edge-enhanced image is obtained by adding the difference image to the original image.

FIG. 16 is a block diagram showing an image contour enhancement processing apparatus using a conventional smoothing filter. The main signal S enters the smoothing filter 70, where image smoothing processing is performed. The output of the smoothing filter is the unsharp signal U obtained by weighted averaging the main signal S of the pixel of interest and the density values of the peripheral pixels. Then, the unsharp mask signal (S−U) is obtained by taking the difference between the main signal S and the unsharp signal U. Two types of correction have been conventionally performed on the obtained unsharp mask signal (S-U).

In the first correction, the first correction section 50 in the figure changes the degree of edge enhancement according to the density value of the main signal S of the target pixel. The output of the first correction unit 50 is the intensity coefficient P. And unsharp mask signal (S-U)
And the intensity coefficient P are multiplied to obtain the contour enhancement signal P (S-
U) is generated. For example, in a highlight portion where noise is noticeable or a shadow portion where electrical noise is large, the value of the intensity coefficient P is reduced in order to reduce noise.

Next, the second correction is performed by the second correction section 60. The strength of the contour emphasis signal is corrected according to the level of the contour emphasis signal P (SU). For example, in a portion where the level of the edge enhancement signal P (SU) is small, this correction is performed for the purpose of further reducing the level and suppressing the graininess of the image.

Then, the finally corrected contour emphasizing signal and the main signal S are added to generate the contour emphasizing signal E.

[0010]

Since the conventional image filter operates in the same manner for all pixels, the smoothing filter also smooths a portion that is not to be blurred, such as an intermediate density region of the image, and sharpens the image. The quantization filter has a disadvantage that it also emphasizes noise components that are often present in the high density region of the image.

Further, in the edge enhancement of an image, there is a demand to enhance the gradation difference (contrast) of the fine line in the highlight portion of the image. In order to emphasize the gradation difference of the thin line in the highlight part, it is necessary to increase the effective mask size in the highlight part. However, such a request cannot be answered by the method using the sharpening filter or the method using the smoothing filter.

That is, in the method of emphasizing the contour of an image using a sharpening filter, the entire original image is processed with the same filter characteristic, and therefore it is not possible to perform another processing only on the highlighted portion. It is possible, and the method of emphasizing the contour of the image using the smoothing filter cannot embody the gradation difference of the thin line in the highlight portion.

Therefore, conventionally, there is a problem that, for example, "the cloth pattern of white clothes is inconspicuous".

The present invention has been made in view of the above problems, and an image filtering method and an image filtering apparatus for changing a filter characteristic for a specific area of an image, and a good contour enhancement for a specific area of the image. It is an object of the present invention to provide an image edge enhancement processing device capable of performing the following.

[0015]

In order to achieve the above-mentioned object, the invention according to claim 1 provides an original image of m × n (m, n
Is a method of performing a predetermined process on the original image by scanning with an image filter of 2 or more), and detecting the density value of the pixel of interest located in the center of the image filter.
However, if the density value is at the highlight level, the image
Image filter to maximize the effective size of the filter.
Determine each weighting factor so that the density value is
Is more effective than highlight level.
Each weighting factor of the image filter
Determine the number, and the density value will change the highlight level and shadow level.
If it is an intermediate level with the
Image so that the effective size is smaller than
It is characterized in that each weighting coefficient of the image filter is determined .

According to a second aspect of the present invention, there is provided a density value detecting means for detecting a density value of a target pixel to be processed, and a target image.
If the density value of the element is at the highlight level, the image
Image filter to maximize the effective size of the filter.
Determine each weighting coefficient and set the density value of the pixel of interest to the shadow
If it's a level, it's a highlight level
Each weight of the image filter is
Determine the weighting coefficient and set the density value of the pixel of interest to
If it is midway between the bell and the shadow level,
Smaller effective size than at shadow level
Decide each weighting factor of the image filter so that it becomes smaller
By Rukoto, and an image filter specifying means for specifying an image filter, and changing the density value of the pixel of interest by using the resulting et <br/> Re that images filtered by an image filter specifying means.

According to a third aspect of the present invention, there is provided an apparatus for performing edge enhancement processing on an original image, comprising density value detecting means for detecting a density value of a target pixel to be processed, and a target pixel of the target pixel.
If the density value is at the highlight level, the image fill
Image filter weights to maximize the effective size of
Determine the weighting coefficient and set the density value of the pixel of interest to the shadow level.
If it is Le, it is more than if it is the highlight level
Image filter weights to reduce effective size
The density coefficient of the pixel of interest is highlighted at the highlight level.
If the level is between the shadow level and the shadow level,
The effective size is much smaller than if it were a do level
To determine each weighting factor of the image filter so that
And by an image filter specifying means for specifying an image filter, a density value changing means for changing the density value of the pixel of interest using the image filter to be identified in the image filter specifying means, the concentration of a change in density value changing means A contour emphasis signal generation unit that generates a contour emphasis signal by subtracting a value from the density value of the target pixel before change, and a contour emphasis completed signal of the target pixel based on the contour emphasis signal obtained from the contour emphasis signal generation unit. And a contour emphasizing means for generating.

[0018]

DETAILED DESCRIPTION OF THE INVENTION

<1. Overview of image filtering processing> m × n (m,
A process of performing image smoothing or sharpening (enhancement of contour) by scanning an image plane in the form of a matrix image filter with n being an integer of 2 or more) is called image filtering.

FIG. 1 is an explanatory diagram showing an outline of image filtering. With respect to the image I shown in FIG. 1A, the X direction is the main scanning direction, and the Y direction is the sub scanning direction, and the pixels to be processed (pixels of interest) OP are sequentially scanned while being scanned pixel by pixel. . At the time of filtering, the pixel of interest OP is located at the center of the image filter F as shown in FIG. In the example of FIG. 1B, 5 × 5
(M = n = 5) matrix image filters are used. Then, the value obtained by performing the weighted average of the density value of the pixel of interest OP and the density values of the peripheral pixels of the pixel of interest based on the weighting coefficient assigned to each component of the image filter is set as the density value of the pixel of interest OP.

[0020] That is, image filter F is the size of the matrix as shown in FIG. 2 (2m + 1) × ( 2n + 1), the weighting coefficient of each component k _-mn to k _mn is assigned . The filtered density value S ′ _xy obtained by filtering the pixel of interest located at the coordinates (x, y) using this image filter F is

[0021]

[Equation 1]

It becomes Here, S _xy is the density value of the pixel located at the coordinates (x, y) of the original image.

In the embodiment of the present invention, the weighting factors k _{-mn to} k _{mn of the} image filter F are determined according to the main signal which is the density value of the pixel of interest before filtering.
Is to be changed. A case where such image smoothing or sharpening (edge enhancement) is performed will be described below.

<2. Smoothing Process> First, the smoothing process will be described. FIG. 3 is a diagram showing an image smoothing filtering device according to an embodiment of the present invention. The main signal S for the pixel of interest is input, and the mask ID extraction unit 10
And the main signal S input to the smoothing processing unit 30 is sent.

The mask ID extraction section 10 outputs an ID value according to the main signal S. The mask ID extraction unit 10 is internally provided with a storage unit such as a memory, and the storage unit holds a table as shown in FIG. 4 in which the relationship between the input and the output is associated with each other. As shown in FIG. 4, when the main signal S is small and is on the shadow side of the image, the ID value to be output is “3”, and when the main signal S is at an intermediate level, the ID value to be output is When it is "1" and the main signal S is the highlight part of the image, the output ID value is "5". In addition, the ID value may be “2” or “4” depending on the level of the input main signal S. Then, the I output from the mask ID extraction unit 10
The D value is sent to the weighting coefficient set selection unit 20.

[0026] In the weighting coefficient set selection section 20, based on the ID value sent from the mask ID extraction unit 10, the determination of all the weighting coefficient k _-mn to k _mn used for image filter. The weighting coefficient set selection unit 20 includes
Similar to the mask ID extraction unit 10, a storage unit such as a memory is provided, and inside each I unit, as shown in FIG.
The smoothing filter in which the weighting coefficient corresponding to the D value is set is stored.

The smoothing filter shown in FIG. 5 has a filter size of 5 × 5 as an example. When the ID value is “1” (see FIG. 5A), the size of the smoothing filter corresponding to the ID value is substantially 3 × 3 image filter. That is, when the weighting coefficient k _ij (i, j is an arbitrary integer) is “0”, the density value of the pixel corresponding to the position (i, j) has no effect on the filtering. Then, the number of weighting coefficients having a weight value of “0” is changed corresponding to the ID value.
In other words, the effective size of the smoothing filter is changed depending on the ID value. Therefore, the mask ID extraction unit 10 and the weighting coefficient set selection unit 20 change the effective size of the smoothing filter according to the density value of the main signal S of the target pixel.

Further, the smoothing filter corresponding to each ID value shown in FIGS. 5A to 5E has a frequency component attenuated to a lower frequency as the ID value changes from "1" to "5". Extend. When the ID value is "1" (see FIG. 5 (a))
Is a smoothing filter with an effective size of 3 × 3.
In the case of the value “5” (see FIG. 5E), the target pixel and the peripheral pixels have the same weighting in the 5 × 5 area, and perfect averaging is performed.

Therefore, using the relationship between the main signal S and the ID value as shown in FIG. 4 in the mask ID extracting section 10,
For the intermediate level of the image, the attenuated frequency component is only high frequency, and for the shadow side of the image, the attenuated frequency component is lower than the intermediate level region. Furthermore, on the highlight side of the image, the lowest frequency component is attenuated.

Then, the smoothing processing unit 30 uses the weighting coefficients of the smoothing filter obtained from the weighting coefficient set selection unit 20 to perform the operation shown in the equation 1 to generate the unsharp signal U. The unsharp signal U is a blur signal obtained by smoothing the main signal S.

As described above, when the smoothing processing shown in this embodiment is performed, the main signal S changes in level according to the signal level.
It is possible to adjust the frequency component that attenuates. For example, it is possible to reduce the frequency components that attenuate in the highlighted portion of the image more than other portions.

<3. Sharpening Process> Next, the sharpening process will be described. FIG. 6 is a diagram showing an image sharpening filtering device according to an embodiment of the present invention. In addition,
The sharpening process is a process for extracting a high frequency component of an image similarly to the contour enhancement process. Therefore, the signal obtained as a result of the sharpening processing is the edge-enhanced signal E in which the edge of the image is emphasized.
Becomes The device configuration is almost the same as that of the smoothing filter. Then, the main signal S for the pixel of interest is input, and the input main signal S is sent to the mask ID extraction unit 10 and the sharpening processing unit 40.

The mask ID extraction unit 10 has the same contents as those described in the smoothing process. An ID value corresponding to the main signal S can be output by referring to a table stored in the internal storage unit that associates the relationship between the input and the output.

The weighting coefficient set selection unit 20 also
The same operation as in the smoothing process is performed, but the weighting coefficients of the filters stored in the storage unit are different. In the smoothing filter in the case of the smoothing process, the value of each weighting coefficient is a positive number, but in the sharpening filter in the case of performing the edge enhancement, the weighting coefficient corresponding to the pixel of interest is generally a positive number. , The weighting coefficients corresponding to the other peripheral pixels are negative numbers. FIG. 7 is a diagram showing an example of a sharpening filter used in the sharpening process (edge enhancement process). Figure 7 (a)
As shown in (e), when the ID value is "1", the effective size is the smallest, and the effective size of the filter increases as the ID value increases. As for the weighting coefficient, only the weight value corresponding to the pixel of interest is a positive number, and the weight values around it are negative numbers. Note that each filter performs weighted averaging of the target pixel and its peripheral pixels according to the respective weighting coefficients, so that I shown in FIG.
When the D value is "1", only the highest frequency range of the high frequency components can be extracted, and when the ID value shown in FIG. Can be extracted.

Then, the sharpening processing section 40 uses the weighting coefficients of these sharpening filters to perform the operation shown in the equation 1 to generate the edge-emphasized signal E. The contour enhanced signal E is a signal obtained by subjecting the main signal S to the contour enhancement processing.

When the sharpening processing shown in this embodiment is performed as described above, the signal level of the main signal S changes according to the signal level.
It is possible to adjust the frequency component whose contour is emphasized. For example, it is possible to lower the frequency components of which the edge is emphasized in the highlighted portion of the image than in other portions.

<4. Overall Configuration of Apparatus> Next, the overall configuration of an apparatus to which the embodiment of the present invention is applied will be described. FIG. 8 is a diagram showing an overall configuration of an apparatus to which the embodiment of the present invention is applied. The image input unit 100 generates digital data about an original image like an input scanner. Then, the generated image data is transferred to the image processing unit 200. The image data is the image processing unit 2.
After being subjected to a predetermined process in 00, the image is output to the image output unit 300 such as an output scanner. The image input unit 100, the image processing unit 200, and the image output unit 300.
An operation input unit 201 and an information display unit 202 are provided to allow the operator to perform a desired operation.

The image processing section 200 is provided with a contour enhancement processing section 250. This contour enhancement processing unit 250
May be the above-described image sharpening filtering device, or may be a contour enhancement processing device using a smoothing filter described later.

<5. Emphasizing gradation difference in highlight part> Here, the concept of emphasizing the gradation difference (contrast) of the thin line in the highlight part of the image in the image edge emphasis will be described. In order to emphasize the gradation difference of the thin line in the highlight part, it is necessary to emphasize the low frequency component in the highlight part, which will be described.

FIG. 9 is an explanatory view showing the concept of emphasizing the gradation difference of the thin line in the highlight portion of the image. When a general contour enhancement process is performed on an image having a density value distribution as shown in FIG. 9A, the result is as shown in FIG. 9B. The correction performed by the first correction unit 50 or the second correction unit 60 illustrated in FIG. 16 described in the related art is a correction for increasing or decreasing the density difference h. However, the thin line in the highlight portion of the image has a blurred edge due to light flare and the like, so that the density difference h is unlikely to occur. Even if such a density difference h is increased, it is difficult to visually distinguish the contrast C between FIGS. 9A and 9B. Further, noise, which is a high frequency component, is emphasized more than the edge.

Therefore, in the highlight portion of the image, the gradation difference of the fine line can be enhanced by increasing the edge width w as shown in FIG. 9C. Then, in order to thicken the contour line in the highlight portion of the image, an image filter having a large effective size may be used when filtering the target pixel in the highlight portion. By doing so, it becomes possible to emphasize the edge of the thin line which is the low frequency component. When the edge enhancement of the image is performed, the highlighting portion of the image can be effectively applied by applying the above-described filtering device regardless of the method using the smoothing filter and the method using the sharpening filter. You can change the size.

For example, in the image sharpening filtering device shown in FIG. 6, the relationship between the input (main signal S) and the output (ID value) in the mask ID extracting section 10 may be set as shown in FIG. As shown in FIG. 4, the ID value becomes “5” in the highlight portion of the image, and the weighting coefficient set selection unit 20 changes the ID value to “5” from the sharpening filters shown in FIGS. 7A to 7E. Select the corresponding sharpening filter (FIG. 7 (e)). When the filtering process is performed using the sharpening filter shown in FIG. 7E, the edge width w shown in FIG. 9C becomes large because the frequency range is lower than the other filters. As a result, the gradation difference (contrast) of the fine line can be emphasized in the highlight portion of the image.

<6. Image Edge Enhancement Processing Device> Next, a description will be given of processing for performing image edge enhancement using a smoothing filter. FIG. 10 is a block diagram of an image edge enhancement processing apparatus showing an embodiment of the present invention. First, the mask ID extraction unit 10 outputs an ID value based on the main signal S of the target pixel. The weighting coefficient set selection unit 2
At 0, the weighting coefficient of each component of the smoothing filter corresponding to the ID value is selected from the configuration of the plurality of smoothing filters stored in the storage unit such as the memory, and is output to the smoothing processing unit 30. The smoothing processing unit 30, generates an unsharp signal U by performing an operation expressed by Equation 1 based on the weighting coefficient k _-mn to k _mn smoothing filter sent from the weighting coefficient set selection section 20 . The processing up to this point is the filtering processing by the smoothing filter described above. For example, the main signal S as shown in FIG.
Is input, the unsharp signal U is
The signal becomes as shown in (b).

Then, the unsharp signal U is subtracted from the main signal S to generate an unsharp mask signal (S-U). This unsharp mask signal (S-U) is shown in FIG.
The signal becomes as shown in 1 (c).

On the other hand, the main signal S is also input to the first correction section 50 and the intensity coefficient P is output. In the first correction unit 50, as shown in FIG. 12, in order to reduce a noticeable noise component in the highlight portion and the shadow portion of the image,
The strength coefficient P is small. Then, the unsharp mask signal (S-U) is multiplied by the intensity coefficient P to obtain the contour enhancement signal P (S-U) as shown in FIG.
Is generated.

Then, in the second correction section 60, as shown in FIG. 13, the corrected contour emphasis signal Q is generated according to the signal level of the contour emphasis signal P (SU). The correction shown in FIG. 13 has the effect of suppressing the graininess of the image that is conspicuous when the value of the contour enhancement signal P (S−U) is small (when it is the shadow portion of the image).

Then, the corrected contour emphasizing signal Q and the main signal S are combined to obtain a contour emphasizing signal E. The contour enhanced signal E is the sum of the main signal S and the corrected contour enhanced signal Q as shown in FIG.

In the case of the image contour enhancement processing apparatus shown in this embodiment, the smoothing processing unit 3 is operated according to the main signal S.
The effective size of the smoothing filter used at 0 can be changed. Therefore, if a smoothing filter having a relatively small effective size is used for the main signal S as shown in FIG. 14A, an unsharp signal U1 as shown in FIG. When a relatively large smoothing filter is used, an unsharp signal U2 as shown in FIG. 14 (c) is obtained. Then, when the edge-enhanced signals E1 and E2 are generated for each case, FIG.
It becomes like (d) and (e). The contour-enhanced signal E1 has a thin contour line because it has been processed by a smoothing filter having a small effective size, and the contour-enhanced signal E2 has a thick contour line because it has been processed by a smoothing filter having a large effective size. Then, by using a smoothing filter having a large effective size in the highlight portion of the image, it is possible to emphasize the gradation difference of the thin line.

Further, such an effect can also be obtained when the contour enhancement of an image is performed by using a sharpening filter. In such a case, similarly, the sharpening filter having a large effective size has a thick contour line, and conversely, the sharpening filter having a small effective size has a thin contour line.

<7. Modification> In the image filtering device for smoothing or sharpening described above, the mask ID extraction unit 10 has an ID value of "1," with respect to the input main signal S as shown in FIG. 2, 3, 4, 5 "
Can take only one of these values.

Therefore, in order to further improve the filtering accuracy, the relationship shown in FIG. 15 is adopted. That is, depending on the density value of the main signal S, an intermediate value below the decimal point, such as an ID value "2.5", can be taken. Then, the ID obtained from the correspondence relationship in FIG.
The value is sent to the weighting coefficient set selection unit 20.

In the weighting coefficient set selection unit 20, the ID
Although the image filter corresponding to the value is selected, only five types of image filters are stored in the storage unit of the weighting coefficient set selection unit 20. Therefore, for example, when the ID value is “2.5” during the smoothing process, as shown in FIG.
An intermediate value of the weighting factors of the ID values “2” and “3” shown in (c) is derived, and the obtained value is used as each weighting factor of the ID value “2.5”.

Next, the description has been given assuming that the signal input to the mask ID extraction unit 10 is the main signal S which is the density value of the pixel of interest. However, the amount of noise included in the main signal S differs for each pixel. For example, there is a case where the shadow portion of the image includes only one pixel with a lot of noise and has the density value of the highlight portion, and when the pixel is processed, it is filtered by the image filter used for the highlight portion. In this case, although the density value is high due to noise, there is a disadvantage that the same processing as the highlight portion is performed.

Therefore, in order to eliminate such an inconvenience, an average value of the main signal S for the target pixel and a plurality of pixels located around the target pixel is calculated, and the average value is input to the mask ID extraction unit 10. It is advisable to adopt a configuration that allows it.
As a result, the amount of noise included in the main signal S is reduced, and highly accurate filtering processing can be performed.

In this embodiment, the case where the image filter is 5 × 5 has been described as an example.
In general, an image filter of m × n (m and n are integers of 2 or more) may be used as long as the pixel of interest to be processed is located at the center of the image filter.

[0056]

As described above, according to the first and second aspects of the invention, the density value of the target pixel is detected and
If the value is at the highlight level, the image filter
Image filter weights to maximize the effective size
If the density coefficient is a shadow level,
Has a smaller effective size than at highlight level.
Determine each weighting factor of the image filter so that it becomes smaller
However, the density value is between the highlight level and the shadow level.
If it's an inter-level, it's a shadow level
Image filter so that the effective size becomes even smaller
In order to determine each weighting coefficient of, the frequency component to be filtered can be changed according to the density value of the pixel of interest located in the center of the image filter .
In particular, the highlight portion of the image can be lower frequency range to be smoothed or sharpened than other portions. Further, it is possible to use an image filter having a different effective size for each pixel. For example, when smoothing processing, the degree of smoothing is reduced for a portion that is not to be blurred, such as an intermediate density range of an image, In the sharpening process, it is possible to reduce the degree of emphasizing a high-density portion of the image that contains a lot of noise.

[0057]

According to the invention of claim 3 , the processing target is
Edge enhancement processing in accordance with the concentration range of interest pixels to be can be realized, it is possible to especially emphasize the gradation difference of thin lines of the highlight portion of the image. Therefore, "white fabric cloth grain" and the like, which are not noticeable by the conventional processing, are also noticeable.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing an outline of image filtering.

FIG. 2 is a diagram showing weighting coefficients of respective components of an image filter.

FIG. 3 is a diagram showing an image smoothing filtering device according to an embodiment of the present invention.

FIG. 4 is a diagram showing a relationship between inputs and outputs in a mask ID extraction unit.

FIG. 5 is a diagram showing a smoothing filter used in a smoothing process.

FIG. 6 is a diagram showing an image sharpening filtering device according to an embodiment of the present invention.

FIG. 7 is a diagram showing a sharpening filter used in a sharpening process.

FIG. 8 is a diagram showing an overall configuration of a device to which an embodiment of the present invention is applied.

FIG. 9 is an explanatory diagram showing a concept of emphasizing a gradation difference of a thin line in a highlight portion of an image.

FIG. 10 is a configuration diagram of an image edge enhancement processing apparatus showing an embodiment of the present invention.

FIG. 11 is a diagram showing signals in each unit of the image edge enhancement processing apparatus.

FIG. 12 is a diagram showing a relationship between input and output in the first correction unit.

FIG. 13 is a diagram showing a relationship between input and output in the second correction unit.

FIG. 14 is a diagram showing a difference between an image filter having a small effective size and an image filter having a large effective size.

FIG. 15 is a diagram showing a modified example of the relationship between input and output in the mask ID extraction unit.

FIG. 16 is a configuration diagram showing a conventional image edge enhancement processing device.

[Explanation of symbols]

10 Mask ID extraction unit 20 Weighting coefficient set selection unit 30 smoothing processing unit 40 Sharpening section 100 image input section 200 Image processing unit 250 Contour enhancement processing unit 300 image output section S main signal U unsharp signal S-U unsharp mask signal P emphasis coefficient P (S-U) contour enhancement signal Q Corrected contour enhancement signal E contour enhanced signal

─────────────────────────────────────────────────── ─── Continuation of the front page (56) References JP-A-3-54679 (JP, A) JP-A-6-62230 (JP, A) JP-A-1-120682 (JP, A) (58) Field (Int.Cl. ⁷ , DB name) G06T 5/20

Claims (3)

(57) [Claims] 1. A method of performing a predetermined process on an original image by scanning the original image with an m × n (m, n is an integer of 2 or more) image filter, the center of the image filter Value of the pixel of interest located in the area
Is detected and the density value is at the highlight level,
To maximize the effective size of the image filter
Determining each weighting factor of the image filter,
Highlight level if the value is a shadow level
So that the effective size is smaller than
Determining each weighting factor of the image filter,
The value is between the highlight level and the shadow level.
If it is
The image fill to reduce the effective size
An image filtering method characterized in that each weighting coefficient of the data is determined . 2. A density value detecting means for detecting a density value of a target pixel to be processed, and a density value of the target pixel at a highlight level.
To maximize the effective size of the image filter
Determine each weighting factor of the image filter,
If the density value of is at the shadow level, the highlight
The effective size will be smaller than if it is a level
Determining each weighting factor of the image filter,
The density value of the pixel of interest is the highlight level and the shadow level
If it is an intermediate level with, it is a shadow level
Before the effective size becomes even smaller than the case before
By determining each weighting factor of the image filter
Te, image filtering apparatus, wherein the image filter specifying means for specifying the image filter comprises, changes the density value of the pixel of interest by using the image filter that obtained by the image filter specifying means. 3. An apparatus for performing contour enhancement processing on an original image, comprising: density value detection means for detecting a density value of a pixel of interest to be processed; and a density value of the pixel of interest is a highlight level. In case
To maximize the effective size of the image filter
Determine each weighting factor of the image filter,
If the density value of is at the shadow level, the highlight
The effective size will be smaller than if it is a level
Determining each weighting factor of the image filter,
The density value of the pixel of interest is the highlight level and the shadow level
If it is an intermediate level with, it is a shadow level
Before the effective size becomes even smaller than the case before
By determining each weighting factor of the image filter
Te, an image filter specifying means for specifying the image filter, the density value changing means for changing the density value of the pixel of interest by using the image filter to be identified in the image filter specifying means, by the density value change means A contour emphasis signal generation unit that generates a contour emphasis signal by subtracting the changed density value from the density value of the pixel of interest before the change, and the attention based on the contour emphasis signal obtained from the contour emphasis signal generation unit. An edge enhancement processing apparatus for an image, comprising: an edge enhancement unit that generates a pixel edge enhanced signal.