JP2599040B2 - Image processing method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for performing image processing for printing plate making and the like, and more particularly to an image processing method for combining two image portions.

[0002]

2. Description of the Related Art Image processing called brush work is performed as a kind of processing for partially correcting an image for printing. A “brush work” is a method in which a point on an image displayed on a monitor is designated with a mouse or the like, and image data of a predetermined shape (referred to as a brush shape) around the designated point is represented by image data. Refers to the process of making corrections.

As one type of brush work, there is image processing called transplantation brush. The transplant brush is a process of changing the image data of the second image portion to the image data of the first image portion by transplanting the image data of the first image portion to the second image portion. That is, the transplanting brush is a process in which the original image data of the second image portion is lost. Here, "image data" means
Data representing color information (for example, hue, lightness, and saturation) of each pixel in the image.

As a process for synthesizing color information of two image portions, a process generally called image synthesis is often used. A process usually called “image composition” is a process of adding image data of two image portions at a specified ratio.

[0005]

As one of the processes based on the image data of the two image portions, for example,
When there are photographs of the first and second objects, it may be required to make the surface of the second object look as if it were the surface of the first object. In order to obtain such an image, it is necessary to reflect the texture and roughness of the first object (the first image portion) to the image of the second object (the second image portion). However, there is a problem that such image processing cannot be performed with the above-described transplant brush or conventional image synthesis.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems in the prior art, and has a third image in which the texture and roughness of the first image portion are reflected in the image of the second image portion. It is an object of the present invention to provide an image processing method capable of obtaining a portion (composite image portion).

[0007]

In order to solve the above-mentioned problems, an image processing method according to a first aspect of the present invention comprises:
(A) extracting a high-frequency component from first brightness component data representing the brightness distribution of the first image portion; and (b)
Extracting a low-frequency component from second brightness component data representing the brightness distribution of the second image portion; and (c) combining the high-frequency component and the low-frequency component to obtain the third image. Generating third brightness component data representing the brightness component of the portion.

In the image processing method according to the present invention, in the step (c), the high-frequency component region in which the high-frequency component of the first image portion is distributed is formed without any gap on the image plane including the second image portion. For each pixel in the low-frequency component region where the low-frequency component of the second image portion is repeatedly arranged virtually and repeatedly, the value of the low-frequency component of the pixel is determined by the position of the pixel in the high-frequency component region. The high frequency component and the low frequency component are synthesized by synthesizing with the value of the high frequency component according to

In the image processing method according to the present invention, when the first and second image portions are color images, a plurality of color images of the first image portion may be provided before the step (a). The first color separation image data represented by the color separation component data of
And converting the second color-separated image data representing the color image of the second image portion by a plurality of color-separated component data into second converted image data including at least second lightness component data. And in the step (c), including the third brightness component data,
Generating the third converted image data representing the color image of the image portion of step (c), and, following the step (c), converting the third converted image data into a third set of a plurality of color separation component data. Inversely converted to the color-separated image data.

Further, in the image processing method according to the fourth aspect, the third converted image data in the step (c) is:
It is composed of component data other than the brightness component data of the components of the second converted image data in the low-frequency component region, and third brightness component data.

[0011]

The high-frequency component of the brightness component data is a component that represents a texture or roughness. Therefore, if the high-frequency component of the lightness component data of the first image portion and the low-frequency component of the lightness component data of the second image portion are combined, the third image reflecting the texture and roughness of the first image portion is obtained. Can be obtained.

According to a second aspect of the present invention, the high-frequency component regions are arranged on an image plane including the second image portion, and the value of the high-frequency component corresponding to the position of the pixel in the arranged high-frequency component region is determined by the low When the value of the low-frequency component of the pixel in the frequency component area is combined with the value of the low-frequency component, the second image portions are specified to overlap with each other a plurality of times on the image plane including the second image portion. Also, the distribution of the high frequency components in the overlapping portions does not change. Therefore, the texture and roughness of the first image portion can be correctly reflected on the third image portion.

According to a third aspect of the present invention, the first and second image portions are color images, and each of the first and second image portions is formed by a plurality of color separation component data. May be represented. In this case, if the converted image data including the lightness component data is generated by converting the color separation image data, and the lightness component data is synthesized with the high frequency component and the low frequency component,
The texture and roughness of the first image portion can be reflected on the third image portion.

Further, in the case of a color image, a fourth aspect of the present invention is provided.
As described in above, the converted image data of the third image portion after the synthesis is configured by using the component data other than the brightness component data among the components of the second converted image data in the low frequency component region. For example, color information other than texture and roughness in the second image portion can be reflected in the third image portion.

[0015]

FIG. 1 is a block diagram showing an image processing apparatus according to one embodiment of the present invention. This image processing device is C
PU 10, high-frequency component memory 20, low-frequency component memory 30, composite image memory 40, color monitor 50
, Keyboard 60, mouse 70, input scanner 8
0, an image disk 90, an output scanner 100, and a fixed pattern memory 110. These components 10 to 110 are connected to each other by a bus line 120. The three memories 20, 3
Reference numerals 0 and 40 are RAMs, and the image disk 90 and the fixed pattern memory 110 are magnetic disks.

The CPU 10 includes an image data conversion unit 11
And high frequency component extracting means 12 and low frequency component extracting means 1
3, a synthesizing unit 14, and an image data inverse conversion unit 15. These means represent the contents of processing executed by the CPU 10 according to a predetermined program.

FIG. 2 is a flowchart showing a processing procedure in this embodiment. In step T1, the two originals are photoelectrically scanned by the input scanner 80, and their image data is read. FIGS. 3A and 3B are conceptual diagrams showing examples of an image M1 of a first document and an image M2 of a second document, respectively. In this example, the skin of the person in the second image is to be reproduced with the apple texture of the first image, but, for example, the first image may be a person with more beautiful skin. . By scanning these images M1 and M2, first image data D1 and second image data D2 are created. The first and second image data D1 and D2 are temporarily stored in the image disk 90. In this embodiment, each of the image data D1 and D2 is
It has four color separation components representing the density of each color of Y (yellow), M (cyan), C (cyan), and K (black).

In the image processing in the following steps T2 to T10, an image data component (high-frequency component, FIG. 3C) representing color information of texture and roughness is extracted from a part of the first image M1. Further, image data components (low-frequency components, FIG. 3D) representing color information other than texture and roughness are extracted from a part of the image portion of the second image M2, and these image data components are extracted. (Fig. 3
(E)). Hereinafter, a detailed method of extracting and synthesizing the image data component will be described.

First, in step T2 of FIG. 2, the first image M1 and the second image M2 are displayed on the color monitor 50, and the operator can use the first image portion R1 (FIG. a)). This designation is performed by using the mouse 70 to designate the position of the center point P1 of the first image portion R1.

In step T3, the image data conversion means 1
1 converts the image data D1 of the first image portion R1 into data representing each of brightness, saturation, and hue. In this conversion, first, respective data Y0, M of YMCK
0, C0, and K0 are converted by the following equation 1, and the K component is added to each component of YMC.

(Equation 1) By the conversion of Expression 1, for example, the data of FIG. 4A is converted as shown in FIG.

Next, the data Y1, M obtained by equation (1)
1, and C1 are converted into data of lightness V1, saturation S1, and hue H1 by Expressions 2 and 3.

(Equation 2)

(Equation 3)

FIG. 5 is an explanatory diagram showing the contents of the conversion by equations (2) and (3). FIG. 5A shows a color solid having the data Y1, M1, and C1 obtained by Expression 1 as orthogonal axes. This color solid has a white point PW as an origin, and a vertex PK opposed to the point PW is a point representing black. FIG. 5 (b)
Represents Y on a plane perpendicular to the axis connecting vertex PK and vertex PW.
The axes Yv, Mv and Cv on which one axis, M1 axis and C1 axis are projected are shown. That is, the Yv axis, the Mv axis, and the Cv axis are images of the Y1, M1, and C1 axes projected on a plane parallel to a plane passing through the three vertices PY, PM, and PC.

In the color solid shown in FIG.
The lightness V of P is defined by a coordinate value when a diagonal line (hereinafter, referred to as a PW / PK axis) from the vertex PW to the vertex PK is used as a coordinate axis. The saturation S is defined as a distance from the PW / PK axis, and the hue H is defined as an angle around the PW / PK axis. However, the hue H has the direction of the Y1 axis set to 0 °. Lightness V1, saturation S1, and hue H1 given by Expression 3
Is data defined as described above.

Returning to FIG. 2, in step T4, the high frequency component extracting means 12 extracts the high frequency component V1h of the brightness data V1 of the first image portion R1. FIG.
It is a conceptual diagram which expands and shows the image part R1. This first image portion R1 has a size of 63 × 63 pixels. The extraction of the high-frequency component of the brightness data V1 is performed according to the first expression of the following Expression 4.

(Equation 4) Here, x and y are coordinates in the first image portion R1.

The operation according to Equation 4 is performed as shown in FIG.
The center pixel of the high-pass filter HF of 3 pixels is positioned at each pixel in the first image portion R1, and the high-pass filter H
This is equivalent to converting the image data of the center pixel by F. The filtering processing using the high-pass filter HF and the low-pass filter described later is described in, for example, Mitsuo Kaji, “Printing Image Engineering for Printing and Electrical Engineers”, June 15, 1988, Printing Society Press Published, pages 251 to 255.

FIG. 6 is a conceptual diagram showing a state where the high-pass filter HF is positioned at the initial position of the first image portion R1. A region indicated by a broken line in FIG. 6 is a region R1a (hereinafter, referred to as a “high-frequency component region”) in which high-frequency components obtained after converting image data of each pixel of the first image portion R1 by the high-pass filter HF are distributed. FIG. The high-frequency component region R1a is a region in which the outermost one pixel layer has been removed from the first image portion R1.
It has a size of 61 × 61 pixels. Further, the brightness data V1h of the high-frequency component region R1a is the data obtained by Expression 4 above.

In step T5, the high-frequency components of the saturation data S1 and the hue data H1 of the first image portion R1 are extracted. The extraction of the high-frequency components S1h and H1h of the saturation data S1 and the hue data H1 is also performed according to the above equation (4).

In step T6, the operator designates the second image portion R2 in the second image M2 (see FIG. 3).
(B)). This designation is performed by using the mouse 70 to designate the position of the center point P2 of the second image portion R2.

In step T7, the low frequency component extracting means 1
3, the image data D2 of the second image portion R2 is converted into data representing each of brightness, saturation, and hue. This is the same as the processing in step T3.

In step T8, a low frequency component of the brightness data V2 of the second image portion R2 is extracted. FIG. 8 is a conceptual diagram showing the second image portion R2 in an enlarged manner. This second image portion R2 also has a size of 63 × 63 pixels, like the first image portion R1. Low frequency component V of brightness data V2
The extraction of 2w is performed according to the following Expression 5.

(Equation 5)

The calculation based on the equation (5) is as shown in FIG.
The center pixel of the x3 pixel low-pass filter LF is positioned at each pixel in the second image portion R2, and the low-pass filter L
This is equivalent to converting the image data of the center pixel by F.

FIG. 8 shows a state where the low-pass filter LF is positioned at the initial position of the second image portion R2. In FIG. 8, a region indicated by a broken line is a region R in which low-frequency components obtained after converting the image data of each pixel of the second image portion R2 by the low-pass filter LF are distributed.
2a (hereinafter, referred to as a “low frequency component region”). The low-frequency component region R2a includes a second image portion R2
Area from which the outermost one pixel layer has been removed.
It has a size of 61 pixels.

In step T9, the low-frequency components S2w and H2 of the saturation data S2 of the second image portion R2 and the hue data H2 are obtained.
Extract w. These low frequency components S2w, H
The extraction of 2w is also performed according to the above equation (5).

In step T10, low-frequency components and high-frequency components are synthesized to create synthesized image data. In creating this composite image data, first, the brightness data V
1h, the saturation data S1h, and the hue data H1h are combined with the lightness data V2w, the saturation data S2w, and the hue data H2w of the low-frequency component, respectively, and thereafter, the combined brightness, saturation, and hue data are obtained. The image data is inversely converted to YMCK image data based on the image data.

High frequency component data V1h, S1h, H1h
And the low frequency component data V2w, S2w, H2w are synthesized according to the following equation (6).

(Equation 6) Here, AX and AY are values given by the following equation 7, X and Y are the coordinates of the pixel in the second image M2,
x and y are the coordinates of the pixel in the low frequency component region R2a.

(Equation 7) Here, dx and dy are the lengths of the sides of the high frequency component region R1a in the x and y directions, and in this embodiment, dx = dy
dy = 61. The operator Int indicates an operation for converting a numerical value in parentheses into an integer. For example, Int (X
/ Dx) is the integer part of the result of dividing the coordinate X by the value dx.

FIG. 9 is an explanatory diagram showing the contents of the calculation represented by Expression 6. FIG. 9A shows the position of the second image portion R2 in the second image M2 and the brush area BR allocated on the image surface of the second image M2 without a gap (the boundary between brush areas is indicated by a broken line). Is shown.). Brush area B
R is a virtual area having the same size as the high-frequency component area R1a, and is repeatedly arranged on the second image M2 so as to cover the screen of the second image M2 without gaps.

As shown in FIG. 9A, the values AX and AY of Expression 6 are the origin OB of the brush area BR to which the pixel PX belongs.
Are coordinates on the image M2. Therefore, (XA
X) and (Y-AY) are respectively equal to the coordinate values of the pixel PX in the brush area BR.

FIG. 9B shows a low frequency component region R2a,
FIG. 4 is an explanatory diagram showing an enlarged brush area BR to which a pixel PX belongs. Since the coordinates of the pixel PX in the brush area BR are (X-AX, Y-AY), V1h (X-AX, Y
-AY), S1h (X-AX, Y-AY), H1h (X
-AX, Y-AY). This is the first term on the right side of Equation 6.

On the other hand, low frequency component data V2w, S2w,
H2w is the internal coordinates (x, y) of the low frequency component region R2a.
Is assigned to each pixel. This is the second term on the right side of Expression 6.

As described above, in synthesizing the image data, the low-frequency component data is allocated according to the “coordinates in the image M2” of each pixel in the low-frequency component region R2a. Data is assigned according to the internal coordinates of each brush area BR repeatedly arranged on the second image M2 without gaps. Then, as the high-frequency component data for each pixel in the low-frequency component region R2a, high-frequency component data for the coordinates of the pixel in the brush region BR is used.

The synthesizing process is performed by adding the high-frequency component data to the low-frequency component data stored in the low-frequency component memory 30 in FIG. The image data thus synthesized is stored in the synthesized image memory 40.

In step T11, the composite image data Vc, Sc, Hc obtained as described above is inversely converted into YMCK color separation image data by the image data inverse conversion means 15. At the time of the inverse conversion, first, the composite image data Vc, Sc, and Hc respectively representing lightness, saturation, and hue are converted into Yv, Mv, and Cv components of FIG.

(Equation 8) However, when Yv <0, Yv = 0. Similarly,
Mv = 0 when Mv <0, Cv when Cv <0
= 0.

Next, the Yv, Mv, and Cv components are converted into the YMC color separation components Yc, Mc, and Cc according to equation (9).

(Equation 9) Further, according to Equation 10, each color separation component Yc of YMC,
A black component (K component) is extracted from Mc and Cc, and YMCK color separation components Ye, Me, Ce, and Ke are generated.

(Equation 10) Here, the operator Min indicates an operation for selecting the minimum value.

The processing of equation (10) is based on the assumption that the color separation components of YMC are Yc, Mc, and Cc shown in FIG. 4B, and these components are represented as shown in FIG. This is the process of converting to. This is generally due to undercolor removal (UC
This is the same as the process called R). The undercolor removal ratio (that is, the ratio of Min (Y, M, C) replaced with the K component) may be set as appropriate.

The color separation image data Ye, Me, Ce, Ke obtained as described above is image data representing a color image in the low frequency component region R2a in the second image M2. The image data Ye, Me, Ce, and Ke are transferred from the composite image memory 40 to the image disk 90 in step T12, and the image data of the low-frequency component region R2a in the image data D2 of the second image M2. But,
The image data is replaced with Ye, Me, Ce, Ke. Thus, the post-combination image shown in FIG.

In the above description, the second image M2 contains the second
Only one image portion R2 was specified. However,
There is a case where the above-described image synthesis processing is desired to be performed over a wide area in the second image M2. In this case, the cursor defined by the mouse 70 may be moved on the second image M2.

FIG. 10 is a conceptual diagram showing how the cursor is moved on the second image M2. For example, when the operator moves the cursor in the direction of the arrow while holding down the button of the mouse 70, the CPU 10 monitors the state of the mouse 70 at a constant cycle and recognizes the positions of the points P21 and P22 as the designated points. Shall be. In this case, two second image portions R2 centered on points P21 and P22
1 and R22 are set respectively. For the image data of the pixel PXa included in both of the two second image portions R21 and R22, the combining process of steps T7 to T10 in FIG. 2 is performed as described below. However, the final synthesized image data of the pixel PXa at the time when the synthesis processing for the second image portion R22 is completed is
As described below, there is no change from the data created in the first second image portion R21.

The image data of the pixel PXa included in both of the two second image portions R21 and R22 is synthesized as follows. First, at the point in time when the first second image portion R21 is designated, the processing of the above-described steps T7 to T10 is performed, and composite image data of the pixel PXa is generated and stored in the composite image memory 40. Then, even when the next second image portion R22 is designated, step T7 is performed.
The processing of T10 is repeated, and the image data of the pixel PXa is synthesized and stored in the synthesized image memory 40. At this time, the composite image data for the second image portion R22 is overwritten with the composite image data for the first second image portion R21.

The composite image data (lightness, saturation, and hue data) created by the composition process for the first second image portion R21 is stored in the composite image memory 40.
The image data D2 of the second image M2 is not changed. Accordingly, the low-frequency component of the pixel PXa extracted in the combining process for the second image portion R22 is the same as the pixel PXa extracted in the combining process for the first second image portion R21.
Is the same as the low-frequency component.

The high-frequency component data is allocated according to the internal coordinates of each brush area BR repeatedly arranged without gaps on the second image M2 as shown in FIG. 9A. Therefore, the data regarding the pixel PXa in the composite image data for the second image portion R22 is the same as the composite image data for the first second image portion R21. Therefore, even if the composite image data for the second image portion R22 is overwritten with the composite image data for the first second image portion R21, the data of the pixel PXa does not change.

As described above, in this embodiment, the brush areas BR are virtually repeatedly arranged without gaps on the second image M2, and high-frequency component data is assigned according to the internal coordinates of each brush area BR. Therefore, even if the second image portion for extracting the low-frequency component is specified to overlap with each other a plurality of times, the composite image data of each pixel in the second image M2 may change. Therefore, there is an advantage that a composite image reflecting the texture and roughness of the first image portion can be obtained.

The present invention is not limited to the above embodiment, but can be implemented in various modes without departing from the gist of the invention. For example, the following modifications are possible.

(1) In the above embodiment, the color-separated image data having the YMCK component is converted into lightness, saturation, and hue data, and the high-frequency component, the low-frequency component, and the brightness, saturation, and hue components are converted. Was added. However, the high-frequency component and the low-frequency component may be added only for the lightness component, and the data of the second image portion may be directly used for the saturation and the hue. This is the brightness,
This is because, of the three types of high-frequency components of saturation and hue, the high-frequency component of lightness is a component that particularly exhibits a texture and roughness.

If the high-frequency components of the saturation and the hue are extracted as in the above-described embodiment, there is an advantage that the texture and roughness of the first image portion can be more effectively reflected.

(2) The image data at the time of extracting the high frequency component and the low frequency component is not limited to the data having the brightness, saturation and hue components, but may be any data having at least the brightness component. This is because the texture and roughness of the first image portion can be transplanted to the second image portion by extracting and adding the high-frequency component and the low-frequency component with respect to the lightness component that remarkably represents the texture and roughness. It is.

The image data having a lightness component includes data representing tristimulus values in the XYZ color system and NTS.
There are various types of image data such as C type image data. Here, the “brightness component” may be a component representing density or luminance. However, in the case of image data having a luminance component, the data representing the luminance component is converted into density level data, and then the high frequency component and the low frequency component of the density level are added. Is inversely converted to data of the luminance level. This is usually
This is because luminance level data cannot be directly added.

(3) In the above embodiment, the first image portion R1 is designated in the first image M1 in order to extract high frequency components. However, the first image portion may be specified in the second image M2. In this case, the first image M1 is unnecessary, and the above-described processing can be performed using only the second image M2.

When two images, the first image M1 and the second image M2, are used, when the mouse 70 is moved, the cursors on the first image M1 and the second image M2 are linked. Then, when the mouse button is pressed, the first image portion and the second image portion may be specified at the same time. In this way, if a large number of designated areas are designated on the two images M1 and M2, the texture and roughness of a wide area on the first image M1 can be easily displayed on the second image M2. There is an advantage that it can be reflected.

(4) Image data of a typical image is stored in the fixed pattern memory 1 as an image portion for extracting high frequency components.
10 (see FIG. 1) in advance, and one of the image data may be selected and read out in step T2. Typical images include patterns such as human skin, wood grain, metal surfaces, cotton, hair, and nets. If such typical image data is prepared in advance, there is no need to prepare these images again and read the image data with a scanner when it is desired to use the texture and roughness of these images. There is an advantage that the processing steps can be shortened.

The processing time can be further reduced by storing the high frequency components of the typical image data instead of storing the image data itself in the fixed pattern memory 110. In this case, steps T3 to T in FIG.
5 is unnecessary.

(5) In the above embodiment, the brush areas BR are repeatedly arranged without gaps on the second image M2. However, the arrangement of the high-frequency components in each brush area takes into account the symmetry as shown in FIG. It is good also as a done pattern. In FIG. 11, the arrow of each brush area is a symbol drawn for convenience to indicate the direction of the brush area.

In FIG. 11, there are four brush areas BR1, BR2, BR3, BR4 which are symmetrical with respect to each other. The second brush area BR2 is symmetric with the first brush area BR1. The third brush area BR3 is the first brush area BR3.
Is symmetric with the brush area BR1. The fourth brush area BR4 is symmetric with the third brush area BR3,
The second brush area BR2 is also symmetric.

As described above, if the pattern of the high-frequency component assigned to each brush area is made symmetrical with respect to the boundary between the brush areas, the distribution of the high-frequency component is distributed over the entire second image. The distribution changes continuously. Therefore, it is possible to prevent the distribution of the high-frequency component from changing abruptly at the boundary of the brush area, and thus there is an advantage that the image quality after the synthesis can be improved.

(6) In the above embodiment, the image portions are combined based on the two color images. However, the present invention can be applied to the case where the image portions of two monochrome images are combined. When applied to a black-and-white image, the above-described conversion of the image data is unnecessary since the image data is only the brightness component. Even in a black-and-white image, since the high-frequency component of the lightness component represents texture and roughness, the texture and roughness of the first image portion can be reflected in the second image portion by applying the present invention. There is an advantage that you can.

(7) In the above embodiment, the first image portion and the second
Although both the image portion and the image portion are square, they may have other graphic shapes such as a circle and a rectangle.

(8) In the above embodiment, the high-pass filter H
F and the low-pass filter LF are each configured by a mask of 3 × 3 pixels. However, in general, the high-pass filter is a first spatial filter composed of Ma × Na pixels (Ma, Na is an integer of 3 or more), and the low-pass filter is Mb × Nb (Mb, Nb is an integer of 3 or more) pixels. What is necessary is just to make it the 2nd spatial filter comprised.

[0067]

As described above, the high-frequency component of the brightness component data is a component representing texture and roughness.
Therefore, according to the image processing method of the first aspect of the present invention, the high-frequency component of the brightness component data of the first image portion is obtained by:
Since the low-frequency component of the brightness component data of the second image portion is synthesized, there is an effect that a third image portion reflecting the texture and roughness of the first image portion can be obtained.

According to the image processing method of the present invention, the high-frequency component regions are arranged on the image plane including the second image portion, and the high-frequency component regions corresponding to the positions of the pixels in the arranged high-frequency component regions are arranged. Since the value and the value of the low-frequency component of the pixel in the low-frequency component area are combined, the second image portions overlap each other a plurality of times on the image plane including the second image portion. , The distribution of the high-frequency components in the overlapping portions does not change. Therefore, there is an effect that the texture and roughness of the first image portion can be correctly reflected on the third image portion.

Further, as described in the image processing method according to the third aspect, the first and second image portions are color images, and the first and second image portions are each composed of a plurality of color separation component data. May be represented by the color separation image data. In this case, converted image data including lightness component data is generated by converting the color separation image data, and if the high frequency component and the low frequency component are synthesized with respect to the lightness component data, the texture of the first image portion is obtained. There is an effect that the roughness can be reflected in the third image portion.

Further, according to the image processing method of the present invention, in the case of a color image, component data other than the brightness component data among the components of the second converted image data in the low frequency component region are used. Since the converted image data of the third image portion after the composition is configured, there is an effect that color information other than texture and roughness in the second image portion can be reflected in the third image portion. .

[Brief description of the drawings]

FIG. 1 is a block diagram showing an image processing apparatus to which an embodiment of the present invention is applied.

FIG. 2 is a flowchart showing a procedure according to the embodiment of the present invention.

FIG. 3 is an explanatory diagram showing a schematic procedure of an image combining process in the embodiment.

FIG. 4 is an explanatory diagram showing conversion of YMCK image data.

FIG. 5 is an explanatory diagram showing conversion from YMCK image data to SVH image data.

FIG. 6 is a conceptual diagram showing a first image portion for extracting a high-frequency component.

FIG. 7 is an explanatory diagram showing a low-pass filter and a high-pass filter.

FIG. 8 is a conceptual diagram showing a second image portion for extracting a low frequency component.

FIG. 9 shows a brush area arranged in a second image and a second brush area;
Explanatory drawing showing an image part.

FIG. 10 is an explanatory diagram showing a case where two second image portions are designated.

FIG. 11 is a conceptual diagram showing an arrangement of brush areas having symmetry.

[Explanation of symbols]

 Reference Signs List 11 image data converting means 12 high frequency component extracting means 13 low frequency component extracting means 14 synthesizing means 15 image data inverse converting means BR brush area R1 first image part R1a high frequency component area R2 second image part R2a low frequency component area V1 brightness data S1 Saturation data H1 Hue data V1h High frequency component S1h High frequency component H1h High frequency component V2w Low frequency component S2w Low frequency component H2w Low frequency component

Claims (4)

(57) [Claims] 1. An image processing method for forming a third image portion after synthesis by synthesizing a first image portion and a second image portion, the method comprising: (a) calculating a brightness distribution of the first image portion; Extracting a high-frequency component from the represented first brightness component data; and (b) a second process representing a brightness distribution of the second image portion.
Extracting low frequency components from the brightness component data of
(C) combining the high-frequency component and the low-frequency component to obtain a third component representing a lightness component of the third image portion.
Generating lightness component data for the image processing method. 2. The image processing method according to claim 1, wherein
In the step (c), the high-frequency component regions in which the high-frequency components of the first image portion are distributed are virtually arranged repeatedly on the image plane including the second image portion without any gap, and the high-frequency component regions of the second image portion are virtually arranged. For each pixel in the low-frequency component region where the low-frequency component is distributed, the value of the low-frequency component of the pixel is combined with the value of the high-frequency component corresponding to the position of the pixel in the high-frequency component region to obtain the high-frequency component. An image processing method for synthesizing a component and the low frequency component. 3. The image processing method according to claim 2, wherein
The first and second image portions are color images, and the step (a)
Prior to the first step, the first color separation image data representing the color image of the first image portion by a plurality of color separation component data is converted into first conversion image data including at least first lightness component data. Converting the second color-separated image data representing the color image of the second image portion by a plurality of color-separated component data into second converted image data including at least second lightness component data; )), Generating third converted image data including a third brightness component data and representing a color image of a third image portion, and further comprising, following the step (c), the third converted image data An image processing method comprising the step of: inversely converting image data into third color-separated image data composed of a plurality of color-separated component data. 4. The image processing method according to claim 3, wherein
The third converted image data in the step (c) is an image processing composed of component data other than the brightness component data among the components of the second converted image data in the low-frequency component region, and the third brightness component data. Method.