JP7205693B2 - Image processing device and computer program - Google Patents

Description

The present specification relates to image processing using image data generated using an image sensor.

The data conversion device disclosed in Patent Document 1 detects pixels of peripheral colors of C, M, and Y in RGB image data, and corrects the values of the detected pixels to the correction colors of C, M, and Y. . The corrected color of C is, for example, a color in which the two components of G and B take the maximum value (255).

JP 2009-159284 A

By the way, an image generated using an image sensor may contain color components different from those on the document due to reading characteristics such as the relationship between halftone dots on the document and the reading position of the image sensor. There is Such color components may degrade image quality.

This specification discloses a technique capable of suppressing degradation in image quality due to reading characteristics of an image sensor when image data generated using the image sensor is used.

The technology disclosed in this specification has been made to solve at least part of the above-described problems, and can be implemented as the following application examples.

[Application Example 1] An image processing apparatus comprising: an image acquisition unit that acquires target image data generated using an image sensor; A region specifying unit that specifies a specific color region, wherein the specific color region corresponds to a primary color that is a color corresponding to one component among three or more components of a specific color system and two components. the first area specifying unit, which is an area to be represented by a specific color that is one of a secondary color that is a color to be displayed, and values of a plurality of pixels in the specified specific color area a correcting unit that performs correction to reduce a specific component of the image to generate corrected image data, wherein the specific component is a component different from the component corresponding to the specific color; An image processing device comprising:

Due to the reading characteristics of the image sensor, image quality may be degraded when a specific color area contains components different from the components corresponding to the primary and secondary colors. According to the above configuration, since the correction for reducing the specific component different from the corresponding component is executed for the values of the plurality of pixels in the specific color area, the specific component is not mixed with the primary color or the secondary color. It is possible to suppress the deterioration of image quality with As a result, when image data generated using an image sensor is used, deterioration in image quality due to reading characteristics of the image sensor can be suppressed.
[Application example 2]
The image processing device according to Application Example 1, further comprising:
a generation unit that uses the corrected image data to generate print data for executing printing using a plurality of color materials;
The image processing device, wherein the correction for reducing the specific component is correction for reducing the amount of use of at least one colorant among the plurality of colorants.
[Application example 3]
The image processing device according to Application Example 2,
The image processing apparatus, wherein the target image data is image data indicating a color for each pixel with a color value of a first color system including components corresponding to a plurality of signals output from the image sensor.
[Application example 4]
The image processing device according to Application Example 3, further comprising:
converting a value of each pixel of the target image data from a color value of the first color system to a color value of a second color system having a plurality of components corresponding to the plurality of color materials; a color conversion unit for generating converted image data,
The correcting unit performs correction to reduce the specific component with respect to the color values of the plurality of pixels in the specific color area in the converted image data in the second color system, thereby performing the correction. An image processing device that generates processed image data.
[Application example 5]
The image processing device according to any one of Application Examples 1 to 4,
The first region specifying unit determines whether each of a plurality of blocks set in the target image is the specific color region using values of a plurality of pixels in the block. An image processing device that identifies the specific color region by making a determination.
[Application example 6]
The image processing device according to any one of application examples 1 to 5, further comprising:
a second region specifying unit that specifies a character region representing a character and a non-character region representing an image different from the character in the target image using the target image data;
a smoothing processing unit that performs smoothing processing on the non-character area;
with
The image processing device, wherein the first region specifying unit specifies the specific color region using the smoothed target image data.
[Application example 7]
The image processing device according to any one of application examples 1 to 6, further comprising:
a second region specifying unit that specifies a character region representing a character and a non-character region representing an image different from the character in the target image using the target image data;
a smoothing processing unit that performs smoothing processing on the non-character area;
with
The image processing device, wherein the correcting unit performs correction for reducing a specific component using the smoothed target image data.
[Application example 8]
The image processing device according to any one of Application Examples 1 to 6,
wherein the specific color region is a region represented by a color whose hue corresponds to one of the primary color and the secondary color and whose saturation and lightness are equal to or higher than a reference; Device.

It should be noted that the technology disclosed in this specification can be implemented in various forms. , a recording medium on which the computer program is recorded, or the like.

1 is a block diagram showing the configuration of a multi-function peripheral 200, which is an example of an image processing apparatus; FIG. 4 is a flowchart of image processing; FIG. 4 is a first diagram showing an example of an image used in image processing; 4 is a flowchart of character identification processing; 4 is a flowchart of color conversion processing; 4 is a flowchart of single determination processing; FIG. 10 is an explanatory diagram of single determination processing; 6 is a flowchart of primary color/secondary color determination processing; 4 is a flowchart of color conversion processing for a specific color area; FIG. 5 is a diagram showing an example of a tone curve used for correction; FIG. 2 is a conceptual diagram of primary color areas of a document;

A. Example:
A-1. Configuration of MFP 200 An embodiment will be described based on an example. FIG. 1 is a block diagram showing the configuration of a multifunction machine 200, which is an example of an image processing apparatus. The MFP 200 includes a CPU 210 that is a processor that controls an image processing device, a volatile storage device 220 such as a DRAM, a nonvolatile storage device 230 such as a flash memory or a hard disk drive, a display unit 240 such as a liquid crystal display, An operation unit 250 including a touch panel and buttons superimposed on a liquid crystal display, an interface (communication IF) 270 for communicating with an external device such as the user's terminal device 100, a print execution unit 280, and a read execution unit 290. , is equipped with

Reading execution unit 290 generates scan data by optically reading a document using a one-dimensional image sensor under the control of CPU 210 . Under the control of the CPU 210, the print execution unit 280 uses a plurality of types of toner, specifically, cyan (C), magenta (M), yellow (Y), and black (K) toners, as colorants. An image is printed on a print medium such as paper using a laser method. Specifically, the print execution unit 280 exposes the photosensitive drum to form an electrostatic latent image, and adheres toner to the electrostatic latent image to form a toner image. The print execution unit 280 transfers the toner image formed on the photosensitive drum onto the paper. In a modified example, the print execution unit 280 may be an inkjet print execution unit that forms an image on paper by ejecting ink as a coloring material.

Volatile storage device 220 provides a buffer area for temporarily storing various intermediate data generated when CPU 210 performs processing. The nonvolatile storage device 230 stores a computer program PG and a color conversion table CT. The computer program PG is a control program that causes the CPU 210 to control the MFP 200 . In this embodiment, the computer program PG and the color conversion table CT are provided in a form pre-stored in the non-volatile storage device 230 when the multifunction machine 200 is manufactured. Alternatively, the computer program PG and the color conversion table CT may be provided in the form of being downloaded from a server, or may be provided in the form of being stored in a DVD-ROM or the like. The CPU 210 can execute image processing, which will be described later, by executing the computer program PG.

A-2. Image Processing FIG. 2 is a flowchart of image processing. This image processing is executed, for example, when the user places a document on the document platen of the reading execution unit 290 and inputs a copy execution instruction. This image processing acquires scan data generated by reading a document using the reading execution unit 290, and uses the scan data to generate print data representing the document, thereby making a so-called copy of the document. This is the process to be implemented.

In S<b>10 , the CPU 210 generates scan data by using the reading execution unit 290 to read the document placed on the platen by the user. The document is, for example, a printed matter in which an image is printed by the MFP 200 or a printer (not shown). The generated scan data is stored in a buffer area of volatile storage device 220 (FIG. 1). The scan data includes a plurality of pixel values, and each of the plurality of pixel values represents the color of the pixel with a color value (also called an RGB value) in the RGB color system. That is, the scan data is RGB image data. The RGB values of one pixel include, for example, three color component values of red (R), green (G), and blue (B) (hereinafter also referred to as R value, G value, and B value). I'm in. In this embodiment, the number of gradations of each component value is 256 gradations.

For example, the image sensor measures the light intensity of each component of R, G, and B through a filter that selectively transmits light of a color to be measured among R, G, and B, and Component signals (R signal, G signal, B signal) are output. Each component value (R value, G value, B value) of the RGB values of the scan data is generated based on the signal of each component output by the image sensor. An RGB value is therefore a color value that includes components corresponding to a plurality of signals output by the image sensor.

Scan data, which is RGB image data, includes three component image data (R component image data, G component image data, and B component image data) corresponding to three color components forming the RGB color system. can be said. Each component image data is image data in which the value of one kind of color component is used as the pixel value.

FIG. 3 is a first diagram showing an example of an image used in image processing. FIG. 3A shows an example of a scanned image SI represented by scan data. Scan image SI includes a plurality of pixels. The plurality of pixels are arranged in a matrix along a first direction D1 and a second direction D2 orthogonal to the first direction D1.

The scanned image SI of FIG. 3A includes a white background Bg1 indicating the background color of the paper of the original, three objects Ob1 to Ob3 different from characters, four characters Ob4 to Ob7, four characters Ob4 to Ob7, and four and a background Bg2 of characters Ob4 to Ob7. An object different from text is, for example, a photograph. Background Bg2 is a uniform image having a color different from white.

In S20, the CPU 210 executes character identification processing on the scan data. The character identification process is a process of classifying a plurality of pixels in the scanned image SI into a plurality of character pixels representing characters and a plurality of non-character pixels not representing characters. As a result, a character area composed of a plurality of character pixels and a non-character area composed of a plurality of non-character pixels are specified.

By the character identification process, for example, binary image data (also referred to as character identification data) is generated in which the value of character pixels is set to "1" and the value of non-character pixels is set to "0". FIG. 3B shows an example of the character identification image TI indicated by the character identification data. In this character identification image TI, a plurality of pixels forming edges of four characters Ob4 to Ob7 in the scan image SI are identified as character pixels Tp4 to Tp7. For relatively large characters, the pixels forming the edges of the characters are identified as character pixels, and for relatively small characters, the entire pixels forming the characters are identified as character pixels. The character identification processing will be described later.

In S30, the CPU 210 executes halftone dot smoothing processing on the scan data to generate smoothed image data representing a smoothed image. Specifically, the CPU 210 executes smoothing processing using a smoothing filter such as a Gaussian filter on each of the values of a plurality of non-character pixels included in the scan data, and performs smoothing processing. Calculate values for a plurality of non-character pixels. Non-character pixels to be smoothed are specified by the character specifying process of S20. The CPU 210 generates smoothed image data including the values of a plurality of character pixels included in the scan data and the values of a plurality of non-character pixels that have been smoothed.

FIG. 3C shows the smoothed image GI represented by the smoothed image data. The smoothed image GI includes a white background Bg1g, objects Ob1 to Ob7 in the scanned image SI, objects Ob1g to Ob7g obtained by smoothing the background Bg2, and a background Bg2g. Of these objects Ob1g to Ob7g and background Bg2g, portions other than the characters Ob4g to Ob7g (also referred to as non-character portions) are smoothed compared to the scanned image SI.

In S40, the CPU 210 executes character sharpening processing on the smoothed image data to generate processed image data. Specifically, the CPU 210 executes sharpening processing such as unsharp mask processing and processing applying a sharpening filter to each of the values of a plurality of character pixels included in the smoothed image data. Values of a plurality of sharpened character pixels are calculated. Character pixels to be sharpened are specified by the character specifying process of S20. Then, the CPU 210 calculates the values of the plurality of non-character pixels (the values of the plurality of non-character pixels that have undergone the smoothing process) and the values of the plurality of character pixels that have undergone the sharpening process, which are included in the smoothed image data. , to generate processed image data including . The values of the plurality of character pixels included in the smoothed image data are the same as the values of the plurality of character pixels included in the scan data because they are not subject to smoothing processing. Therefore, it can be said that the character sharpening process of this step is performed on the values of a plurality of character pixels included in the scan data.

FIG. 3D shows the processed image FI indicated by the processed image data. The processed image FI includes a white background Bg1f, objects Ob1 to Ob7 in the scanned image SI, objects Ob1f to Ob7f corresponding to the background Bg2, and a background Bg2f. Among these objects Ob1f to Ob7f and the background Bg2f, the edges of the characters Ob4f to Ob7f are sharpened compared to the characters Ob4 to Ob7 in the scanned image SI and the characters Ob4g to Ob7g in the smoothed image GI. there is Objects Ob1f to Ob3f other than characters and background Bg2f are not sharpened.

As can be seen from the above description, the objects Ob1f to Ob7f and the background Bg2f in the processed image FI include sharpened characters and smoothed non-characters.

In S45, the CPU 210 executes color conversion processing on the processed image data. Color conversion processing is processing for converting processed image data, which is RGB image data, into CMYK image data. CMYK image data is image data that indicates the color of each pixel using CMYK values. The CMYK values are color values of a color system (also called a CMYK color system) having a plurality of components (C, M, Y, and K components) corresponding to a plurality of colorants used for printing. The color conversion processing of this embodiment includes correction processing for removing color turbidity in areas representing the six colors of C, M, Y, R, G, and B. FIG. This color conversion processing will be described later.

In S50, the CPU 210 executes print data generation processing including halftone processing on the processed image data after the color conversion processing. Specifically, halftone processing is performed on the processed image data (CMYK image data) after color conversion processing, and the dot formation state is determined for each color material used for printing and for each pixel. Dot data shown is generated. The dot formation state can take, for example, two types of dot states and no dot states, and four types of states of large dots, medium dots, small dots, and no dots. Halftone processing is performed, for example, according to a dither method or an error diffusion method. The dot data are rearranged in order of use at the time of printing, and print data is generated by adding a print command to the dot data.

In S60, the CPU 210 executes print processing and ends the image processing. Specifically, CPU 210 supplies print data to print execution unit 280 and causes print execution unit 280 to print the processed image.

According to the image processing described above, the character sharpening process is executed on the values of the plurality of character pixels Tp4 to Tp7 that form the specified character area in the scan data (S40). A dot smoothing process is performed on the character pixel values (S30) to generate processed image data. As a result, different image processing is performed on the value of the character pixel and the value of the pixel different from the character pixel, so appropriate image processing for the scan data can be realized. Note that in a modified example, the character sharpening process of S40 may be performed first, and then the halftone dot smoothing process of S30 may be performed.

More specifically, processed image data including a plurality of sharpened text pixel values and a plurality of smoothed non-text pixel values is generated (S30, S40). . As a result, processed image data representing a processed image FI with a good appearance can be generated.

For example, as shown in the processed image FI of FIG. 3D, in the processed image data, sharpened values are used as the values of the character pixels. As a result, the characters in the processed image FI look sharp, so that, for example, the appearance of the printed processed image FI can be improved.

In the processed image data, smoothed values are used for the values of the background Bg2 in the processed image FI and the values of the non-character pixels that constitute objects such as photographs that are different from characters. As a result, it is possible to suppress, for example, halftone dots that cause moire from appearing in a portion of the processed image FI that is different from the characters, thereby suppressing the occurrence of defects such as moire in the processed image FI to be printed. can. As a result, the appearance of the processed image FI to be printed can be improved.

For example, a document used to generate scan data is a printed matter on which an image is printed. Therefore, for example, a uniform portion such as the background Bg2 having a color different from white in the document forms a halftone dot when viewed at the dot level for forming an image. A halftone dot includes a plurality of dots and a portion where no dots are arranged (portion indicating the background color of the document). For this reason, when viewed at the pixel level, halftone dots are shown in the area indicating the background Bg2 in the scan image SI. Dots in halftone dots are arranged with periodicity due to the influence of a dither matrix used when printing an original. For this reason, when printing is performed using scan data, the halftone dot periodic component existing in the original image (scanned image SI) before halftone processing and the halftone dot dot composing the printed image Moire tends to appear due to interference between periodic components and . In the processed image FI of the present embodiment, the smoothing process reduces periodic components of dots in portions different from edges in the original image (scanned image SI). As a result, when the processed image FI is printed using the processed image data, for example, moiré can be suppressed from occurring in the printed processed image FI.

In particular, in the above image processing, the processed image data is used to generate the print data (S50). Therefore, for example, appropriate print data capable of suppressing moiré that tends to occur in the processed image FI to be printed is generated. can do.

A-3. Character Identification Processing The character identification processing of S20 in FIG. 2 will be described. FIG. 4 is a flowchart of character identification processing. At S200, the CPU 210 uses the scan data to generate luminance data. Specifically, luminance data representing the luminance of each pixel of the scanned image SI is generated by converting the color value (RGB value) of each pixel of the scanned image SI into the luminance Y using a predetermined conversion formula. be. The brightness Y is calculated using RGB values (R, G, B), for example, by the formula brightness Y=((0.298912×R)+(0.586611×G)+(0.114478×B)). In this example, each component value of the RGB values and the luminance Y are gradation values of 256 gradations from 0 to 255.

In S220, the CPU 210 executes edge extraction processing on the luminance data to generate edge extraction data. Specifically, the CPU 210 applies a so-called Sobel filter to the value of each pixel of the smoothed minimum component data to calculate the edge strength Se. The CPU 210 generates edge extraction data in which these edge intensities Se are values of a plurality of pixels.

In S240, the CPU 210 executes binarization processing on the edge extraction data. Thereby, binary image data is generated as character identification data. For example, in the edge extraction data, the CPU 210 classifies pixels whose pixel values (that is, edge strength) are equal to or greater than a threshold as text pixels, and classifies pixels whose pixel values are less than the threshold as non-text pixels. . In the binary image data, as described above, the value of character pixels is "1" and the value of non-edge pixels is "0".

In the character identification data described above, edges such as graphic lines that are different from characters can also be identified as character pixels. In this case, the edges that are not the characters are subject to the character sharpening process in S40, but there is no problem because the lines of graphics and the like have a sharp appearance. As a modification, another character specifying process devised to specify a character region (character pixels) with higher accuracy may be employed. For example, the CPU 210 identifies an area where the edge strength is greater than the reference value, and identifies that area as the object area. The CPU 210 determines whether an object in each object area is text or an object other than text (for example, a photograph or drawing). The object type is determined based on the color distribution of each object area (for example, the number of colors that the object has) and the ratio of the pixels that make up the object to the total number of pixels in the rectangle that circumscribes the object. will be Various known methods can be adopted as the method of specifying such a character region, and known methods are disclosed in, for example, Japanese Patent Application Laid-Open No. 5-225378 and Japanese Patent Application Laid-Open No. 2002-288589. .

A-4. Color Conversion Processing The color conversion processing of S45 in FIG. 2 will be described. FIG. 5 is a flowchart of color conversion processing. In S300, the CPU 210 executes single determination processing on the processed image data. The single determination process divides the processed image FI into a plurality of blocks BL, and determines whether each block BL is a single area representing a single color or a non-single area representing multiple colors. This is the processing for determination. Since the processed image data is image data based on scan data, the processed image FI contains color unevenness due to blurring during reading. A single area is not only an area made up of pixels with a single color value, but can be regarded as representing a substantially single color even if it cannot be said to be a completely single color due to color unevenness, etc. Including area.

FIG. 6 is a flow chart of single determination processing. FIG. 7 is an explanatory diagram of the single determination process. In S410, the CPU 210 divides the processed image FI, which is the image to be processed, into a plurality of blocks BL. FIG. 7A shows a partial image PI that is part of the processed image FI. As shown in FIG. 7A, the processed image FI is divided into a plurality of blocks BL arranged in a matrix. One block BL includes, for example, (m×n) pixels (m in length×n in width) (m and n are integers equal to or greater than 2). For example, m and n are five.

In S420, the CPU 210 selects one block of interest from a plurality of blocks BL set in the processed image FI.

In S430, the CPU 210 classifies a plurality of pixels in the block of interest into one of eight basic colors (C, M, Y, R, G, B, K (black), W (white)). FIG. 7B shows the RGB color space CP. The eight vertices of the cube RGB color space CP correspond to the eight basic colors. That is, the eight vertices are K point Vk (0,0,0), R point Vr (255,0,0), G point Vg (0,255,0), B point Vb (0,0,255 ), C point Vc (0, 255, 255), M point Vm (255, 0, 255), Y point Vy (255, 255, 0), W point Vw (255, 255, 255)). The numbers in parentheses are the values of each color component (R, G, B). The RGB color space CP is defined by three planes F1-F3 into eight cubic spaces CSc, CSm, CSy, corresponding to the eight basic colors C, M, Y, R, G, B, K, W; It can be divided into CSr, CSg, CSb, CSk and CSw. The plane F1 is a plane perpendicular to the R-axis AXr and passing through the midpoint Mr (128, 0, 0) of the R-axis AXr. The plane F2 is a plane perpendicular to the G-axis AXg and passing through the midpoint Mg (0, 128, 0) of the G-axis AXg. A plane F3 is a plane perpendicular to the B-axis AXb and passing through the midpoint Mb (0, 0, 128) of the B-axis AXb. For example, the hatched space CSc in FIG. 7B is the space corresponding to cyan (C). For example, among the plurality of pixels in the block of interest, pixels located in the space CSc whose corresponding RGB values in the scanned image SI correspond to C are classified as C.

FIG. 7(C) shows a histogram indicating the result of classification of a plurality of pixels in the block of interest. As a result of the classification in S430, histogram data as shown in FIG. 7C is generated.

In S440, CPU 210 determines the representative color of the block of interest based on the pixel classification result. Specifically, among the eight basic colors, one or more colors into which the number of pixels equal to or greater than the threshold TH are classified in the histogram are determined as the representative colors of the region of interest. In the example of FIG. 7C, among the eight basic colors C, M, Y, R, G, B, K, and W, the number of pixels classified into C (cyan) and B (blue) is It is equal to or greater than the threshold TH. Therefore, in the example of FIG. 7C, C and B are determined as the representative colors of the attention area. The number of determined representative colors is not limited to two, but may be one or three or more. The threshold TH is set, for example, to about 20% of the total number of pixels in the block of interest.

In S450, CPU 210 determines whether or not the determined number of representative colors of the block of interest is one. If the number of representative colors in the target area is one (S450: YES), the target block is considered to be an area representing a single color. Therefore, in this case, at S460, CPU 210 determines the block of interest as a single area. If the number of representative colors of the block of interest is not one (S450: NO), the block of interest is considered to be an area representing a plurality of colors. Therefore, in this case, at S470, CPU 210 determines the block of interest as a non-single area.

In S480, the CPU 210 determines whether or not all blocks BL in the processed image FI have been processed as target blocks. If there is an unprocessed block BL (S480: NO), the CPU 210 returns the process to S420. When all blocks BL have been processed (S480: YES), the CPU 210 terminates the single determination process.

At this point, all blocks in the processed image FI are determined to be either single-region or non-single-region. For example, blocks BLa and BLc in FIG. 7A include images showing uniform backgrounds BG1f and Bg2f. For this reason, these blocks BLa and BLc are determined as a single area. Block BLb also includes characters Ob4f and background Bg2f. For this reason, the block BLb is determined as a non-single area. Also, although illustration is omitted, for example, when the objects Ob1f to Ob3f of the processed image FI include a portion representing a single color, the block BL indicating that portion is determined as a single area. Also, since the edge portions of the objects Ob1f to Ob3f show a plurality of colors, the blocks BL showing the edge portions are determined as non-single regions.

In S310 of FIG. 5, the CPU 210 executes primary color/secondary color determination processing. The primary color/secondary color determination process is a process of determining whether or not each of a plurality of single areas determined by the single determination process is a specific color area. The specific color area includes a primary color area and a secondary color area. The primary color area is an area to be represented by a color (also called a primary color) corresponding to one of the C, M, and Y components of the CMYK color system. The primary color areas include a cyan area to be represented in cyan, a magenta area to be represented in magenta, and a magenta area to be represented in yellow. A secondary color area is an area to be represented by colors (also called secondary colors) corresponding to two of the C, M, and Y components. In this embodiment, the secondary colors are blue (B) corresponding to C and M, red (R) corresponding to M and Y, and color corresponding to Y and C. and green (G). Thus, the secondary color areas include blue areas to be represented in blue, red areas to be represented in red, and green areas to be represented in green. A secondary color corresponding to two components can also be said to be a color obtained by mixing two primary colors corresponding to the two components.

FIG. 8 is a flowchart of primary color/secondary color determination processing. In S510, the CPU 210 selects one target single area from the plurality of single areas determined by the single determination process.

In S515, the CPU 210 calculates the average color value of the single region of interest. Specifically, average color values (R_av, G_av, B_av) of all pixel values (RGB values) in the single region of interest are calculated.

In S525, the CPU 210 converts the average color value (RGB value) of the single region of interest into a color value of the HSV color system (also called HSV value) using a known conversion formula. Each HSV value includes three component values: an H value that indicates hue, an S value that indicates saturation, and a V value that indicates brightness. The H value ranges from 0 degrees to 360 degrees. The S value ranges from 0% to 100%, with 0% indicating achromatic color. The V value ranges from 0% to 100%, with 0% indicating black and 100% indicating white.

In S530, the CPU 210 determines whether the saturation (S value) of the converted average color value (HSV value) is equal to or greater than the threshold THs. The threshold THs is, for example, 50%. When the saturation (S value) is less than the threshold THs (S530: NO), in S550, CPU 210 determines the single region of interest as a non-specific color region.

If the saturation (S value) is equal to or greater than the threshold THs (S530: YES), in S535 the CPU 210 determines that the lightness (V value) of the converted average color value (HSV value) is equal to or greater than the threshold THv. or not. The threshold THv is, for example, 50%. If the lightness (V value) is less than the threshold THv (S535: NO), in S550 CPU 210 determines the single region of interest as a non-specific color region.

If the lightness (V value) is equal to or greater than the threshold THv (S535: YES), in S540, the CPU 210 determines whether the hue (H value) of the converted average color value (HSV value) is within a predetermined primary color range. , or whether it is within a predetermined secondary color range. The primary color range is, for example, a range of values from 0 degrees to 360 degrees that correspond to the C, M, and Y hues. The secondary color range is a range of values from 0 degrees to 360 degrees that correspond to the R, G, and B hues. For example, the primary color range of C, M, and Y is a range of a predetermined width (for example, 30 degrees) around the H value indicating the hue of C, M, and Y, respectively. For example, the secondary color range of R, G, and B is a range of a predetermined width (for example, 30 degrees) around the H value indicating the hue of R, G, and B, respectively.

If the hue (H value) of the average color value (HSV value) after conversion is neither in the primary color range nor in the secondary color range (S540: NO), the average color value is the primary color or the secondary color. Absent. Therefore, in this case, in S550, CPU 210 determines the single region of interest as the non-specific color region.

If the hue (H value) of the average color value (HSV value) after conversion is in the primary color range or in the secondary color range (S540: YES), the average color value is the primary color or the secondary color. be. Therefore, in this case, in S545, CPU 210 determines the single region of interest as the specific color region.

In S555, the CPU 210 determines whether or not all the single regions in the processed image FI have been processed as single regions of interest. If there is an unprocessed single area (S555: NO), the CPU 210 returns the process to S510. If all single regions have been processed (S555: YES), CPU 210 advances the process to S560.

At this point, every single region in the processed image FI is determined to be either a specific color region or a non-specific color region. For example, the block BLa in FIG. 7A is a single area, but if the background Bg2f is a uniform primary color or secondary color background, the block BLa is determined as a specific color area. Also, the block BLc is a single area, but since the background Bg1f is white, it is determined as a non-specific color area. Further, although illustration is omitted, for example, when a part of the objects Ob1f to Ob3f of the processed image FI is determined as a single area, the single area is divided into a specific color area and a non-specific color area. is determined by either

In S560, the CPU 210 determines all non-single areas as non-specific color areas. As a result, all target blocks in the processed image FI are determined to be either specific color areas or non-specific color areas.

In S320 of FIG. 5, the CPU 210 performs color conversion processing on the non-specific color area of the processed image FI. Specifically, the values (RGB values) of a plurality of pixels included in the non-specific color area are converted to CMYK values pixel by pixel with reference to the color conversion table CT (FIG. 1). The color conversion table CT is, for example, a known lookup table that defines the correspondence between RGB values and CMYK values.

In S330, the CPU 210 executes color conversion processing on the specific color area of the processed image FI. Color conversion processing for specific color regions is different from color conversion processing for non-specific color regions. FIG. 9 is a flowchart of color conversion processing for a specific color area.

At S610, the CPU 210 selects one specific color area of interest from among the plurality of specific color areas determined by the primary color/secondary color determination process of S310 of FIG.

In S615, the CPU 210 determines one CMYK value (also called a corresponding CMYK value) corresponding to the average color value (RGB value) of the specific color region of interest. The average color value calculated in S515 of FIG. 8 is used as the average color value (RGB value). Corresponding CMYK values are determined with reference to the color conversion table CT.

At S620, the CPU 210 converts the values of all pixels in the specific color region of interest into one corresponding CMYK value. For this reason, the converted values of all pixels in the specific color region of interest become one CMYK value common to all the pixels.

In S630, CPU 210 determines whether the specific color area of interest is a primary color area or a secondary color area. For example, if the H value of the average color value (HSV value) of the specific color area of interest is in the primary color range, the specific color area of interest is determined to be the primary color area, and if the H value is in the secondary color range , the specific color area of interest is determined to be a secondary color area. If the specific color area of interest is a primary color area (S630: YES), in S640 the CPU 210 selects the component corresponding to the primary color among the four component values of the CMYK values of each pixel in the specific color area of interest. corrects for three different component values. For example, if the specific color area of interest is a cyan (C) area, three component values (that is, M value, Y value, and K value) excluding the C value are subject to correction. Similarly, when the specific color area of interest is a magenta (M) area, the C value, Y value, and K value are subject to correction. When the specific color area of interest is a yellow (Y) area, the C value, M value, and K value are subject to correction.

FIG. 10 is a diagram showing an example of a tone curve used for correction. The CPU 210 applies the tone curve of FIG. 10 to each correction target component value of each pixel in the specific color region of interest. As a result, each component value to be corrected is reduced and converted to a smaller value than before correction. Specifically, all component values to be corrected that are less than the threshold Vth (eg, 10) are converted to the minimum value (0). A component value to be corrected that is greater than or equal to the threshold value Vth and less than 255 is converted to a value smaller than before correction according to the tone curve. In the tone curve, the maximum value (255) is maintained as the maximum value (255) even after conversion, but in the CMYK values of the primary color area, each component value to be corrected is a relatively small value, It is considered not to be the maximum value.

If the specific color area of interest is a secondary color area (S630: NO), in S650 the CPU 210 selects the secondary color among the four component values of the CMYK values of each pixel in the specific color area of interest. Correct two component values that differ from the corresponding component. For example, if the non-specific color area is a blue (B) area, two component values (ie, Y value and K value) excluding C value and M value are subject to correction. Similarly, when the specific color area of interest is a red (R) area, the C value and K value excluding the M value and Y value are subject to correction. When the specific color area of interest is the green (G) area, the M value and K value excluding the Y value and C value are subject to correction.

The CPU 210 applies the tone curve of FIG. 10 to each correction target component value of each pixel in the specific color region of interest. As a result, each component value to be corrected is reduced and converted to a smaller value than before correction.

In S660, the CPU 210 determines whether or not all the specific color areas in the processed image FI have been processed as the target specific color area. If there is an unprocessed specific color area (S660: NO), the CPU 210 returns the process to S610. When all the specific color areas have been processed (S660: YES), the CPU 210 terminates the color conversion process for the specific color areas.

When the color conversion process for the specific color area is finished, the RGB values of all pixels of the processed image FI are converted to CMYK values, and the color conversion process of FIG. 5 is finished.

In the embodiment described above, the CPU 210 that executes the process (S10 in FIG. 2) of acquiring scan data generated using an image sensor as target image data is an example of an image acquisition unit. The CPU 210 that uses the scan data to execute the processing (S30 and S40 in FIG. 2, S300 and S310 in FIG. 5, FIG. 6 and FIG. 8) for specifying a specific color region in the processed image FI based on the scan data, It is an example of a first region specifying unit. A specific color area is an area to be represented by a specific color that is either a primary color or a secondary color. Processing to generate corrected image data (processed image data after color conversion processing) by performing correction to reduce specific components for the values of multiple pixels in the specified specific color area (Fig. 5 (S330, FIG. 9) is an example of the correction unit. As described above, the specific component is a component (eg, M, Y, K components) different from the component (eg, C component) corresponding to the specific color (eg, C).

Due to the reading characteristics of the image sensor, image quality may be degraded when a specific color area contains components different from the components corresponding to the primary and secondary colors. According to this embodiment, since the correction for reducing the specific component is performed on the values of a plurality of pixels in the specific color area such as the primary color and the secondary color, the specific component is mixed in the primary color and the secondary color. By doing so, it is possible to suppress deterioration in image quality. As a result, when image data generated using an image sensor is used, deterioration in image quality due to reading characteristics of the image sensor can be suppressed.

For example, consider the case of reading a yellow (Y) document, which is the primary color of the CMYK color system. In this case, in principle, the RGB values of the pixels of the scan data are such that the R value and the G value are large values close to the maximum values, and the B value is zero. However, the B value can also be a value greater than 0 due to the characteristics of the image sensor (for example, spectral characteristics of RGB filters).

Also, the manuscript is generally a printed matter in many cases. Scan data obtained by reading the Y area of a document, which is a printed matter, tends to include RGB values including a B value greater than zero. FIG. 11 is a conceptual diagram of the primary color areas of the document. A Y area YA of a document, which is a printed matter, is a uniform Y area from a macroscopic point of view, but is a halftone dot area from a microscopic point of view, as shown in FIG. A halftone dot area is an area in which an area in which Y dots DT made of a coloring material such as ink or toner are formed and a white area WA in which the base color (white) of the paper is exposed are mixed. For this reason, when the image sensor reads the Y dots DT, the B value of the generated RGB values is approximately 0, but when the image sensor reads the white area WA, the B value of the generated RGB values is approximately 0. The value is greater than 0. Due to such reading characteristics of the image sensor, the document is read even though the area of Y on the document should be represented by Y when output by printing, for example. In the scan data, B values greater than 0 are mixed with the RGB values in the Y area. Due to this B value, for example, in an image indicated by the scan data or an image printed using the scan data, an area to be represented by Y may become cloudy. Such inconveniences are not limited to the Y area, but include other primary colors of the CMYK color system such as cyan (C) and magenta (M), and secondary colors of the CMYK color system such as R, G, and B. It happens though. According to this embodiment, it is possible to reduce the turbidity of such primary colors and secondary colors, thereby suppressing deterioration in image quality.

Furthermore, in the above embodiment, the corrected image data (processed image data after color conversion processing) is used to generate print data for printing using CMYK color materials (S50 in FIG. 2). ) is an example of the generator. The correction for reducing specific components is correction for reducing specific components corresponding to CMYK colorants (FIG. 10). Therefore, it can be said that the correction is a correction for reducing the usage amount of at least one of the CMYK colorants. As a result, when printing is performed using scan data, it is possible to suppress deterioration in the image quality of the printed image due to the reading characteristics of the image sensor.

For example, a primary color (eg, C) in the CMYK color system is a color that should be printed using only one type of colorant (eg, C colorant) corresponding to the primary color. For this reason, when a small amount of colorants (for example, M, Y, and K colorants) different from the colorants corresponding to the primary colors are mixed in the printed image in the primary color areas, for example, three or more kinds of Compared with the color area to be printed using the coloring material, the color turbidity due to the small amount of coloring material is easily noticeable. Similarly, a secondary color (for example, B) in the CMYK color system is a color that should be printed using only two types of colorants (for example, C and M) corresponding to the secondary color. For this reason, when a small amount of colorants (for example, Y and K colorants) different from the colorants corresponding to the secondary colors are mixed in the printed image in the secondary color region, for example, three kinds of Compared with the color area to be printed using the above coloring materials, the color turbidity due to the small amount of coloring materials is easily noticeable. According to the present embodiment, it is possible to appropriately reduce the turbidity of the primary colors and secondary colors in which the turbidity of the colors tends to be conspicuous, so that it is possible to effectively suppress the deterioration of the image quality of the printed image.

Furthermore, according to the above embodiment, the scan data as the target image data is RGB image data that indicates the color of each pixel with the color values (RGB values) of the RGB color system. In the RGB color system, the primary colors are R, G, and B, and the secondary colors are C, M, and Y. Therefore, the specific color can also be said to be a primary color or secondary color of the RGB color system. Also, the RGB color system is a color system including components corresponding to a plurality of signals (R signal, G signal, B signal) output by the image sensor. As described above, according to this embodiment, among the three component values of RGB, the component value that should ideally be 0 becomes a value greater than 0 due to the reading characteristics of the image sensor. In addition, correction is performed so as to reduce the component value that should be 0, so it is possible to effectively suppress deterioration in the image quality of the image based on the scan data.

Furthermore, according to the above embodiment, the CPU 210 as the color conversion unit executes color conversion processing for converting the pixel values of the processed image data from RGB values to CMYK values (S320 in FIG. 5, S620 in FIG. 9). ). The CPU 210 as a correction unit performs correction for reducing the specific component on the CMYK values of a plurality of pixels within the specific color area (S640, 650 in FIG. 9). As a result, for example, compared to the case where the RGB values are corrected, the usage amount of the colorant corresponding to the specific component can be appropriately reduced. Therefore, it is possible to effectively suppress the deterioration of the image quality of the printed image due to the mixture of a small amount of the coloring material corresponding to the specific component.

Furthermore, according to the above-described embodiment, the CPU 210 as the first area specifying unit uses the values of the pixels in each of the blocks BL set in the processed image FI to It is determined whether or not the block BL is a specific color area (S300, S310 in FIG. 5, FIGS. 6 and 8). Thereby, the specific color area is specified. As a result, the specific color area within the processed image FI can be appropriately specified.

Furthermore, in the above-described embodiment, the CPU 210 that executes the process (S20 in FIG. 2) of specifying character areas and non-character areas in the scanned image SI using scan data is an example of the second area specifying unit. . The CPU 210 that executes the process (S30 in FIG. 2) of executing the smoothing process on the non-character area is an example of the smoothing processing section. The CPU 210 identifies the specific color area using the smoothed scan data (that is, the processed image data) (S300, S310 in FIG. 5). As a result, since the specific color area is specified using the smoothed scan data, the specific color area included in the halftone dot area can be specified more appropriately than when the scan data before the smoothing process is used. can be done.

For example, as described above, even if the halftone dot area is uniform from a macroscopic point of view, it is uneven from a microscopic point of view because the color of the dots (halftone dots) and the ground color of the paper are mixed. For this reason, if the single determination process of FIG. 5 is performed on the scan data before the smoothing process, a uniform area may not be appropriately determined as a single area from a macro viewpoint. In this case, it may not be possible to appropriately determine a uniform primary color or secondary color region as the specific color region from a macro viewpoint. According to this embodiment, since smoothed scan data is used, the single determination process is performed after the halftone dot area has been made uniform even from a micro viewpoint. As a result, a uniform primary color or secondary color area can be appropriately determined as the specific color area from a macro viewpoint.

Furthermore, according to the above embodiment, the CPU 210 uses the smoothed scan data to perform correction to reduce the specific component (S330 in FIG. 4). As a result, the image quality of the specific color region after correction can be improved more than when using scan data before smoothing processing. For example, if the image to be corrected has a relatively wide portion of a specific color (primary color or secondary color), a plurality of specific color areas of the same color are continuously arranged in that portion. If there is color unevenness in that portion, color differences may occur between a plurality of specific color regions after correction. In this case, block noise may occur in the corrected image and the image quality may be degraded. According to this embodiment, by performing the smoothing process on the scan data, it is possible to reduce color unevenness that occurs in a portion where the specific color regions are continuously arranged. Therefore, by performing correction for reducing the specific component using the smoothed scan data, it is possible to suppress the occurrence of block noise in the corrected image and improve the image quality of the corrected image.

Furthermore, according to the above embodiment, the specific color region is a range in which the hue (e.g., H value) corresponds to either the primary color or the secondary color, and the saturation (e.g., S value) and lightness (eg, V value) is a region represented by a color greater than or equal to a reference (S530 to S550 in FIG. 8). In areas where the saturation and lightness are equal to or higher than the standards, deterioration in image quality is more noticeable than in areas where the saturation and lightness are below the standards when a specific component is mixed with the primary color or secondary color. According to the above configuration, it is possible to effectively suppress deterioration of image quality in a specific color region in which deterioration of image quality is likely to be conspicuous.

As can be seen from the above description, the RGB color system is an example of the first color system, and the CMYK color system is an example of the second color system.

B. Modification (1) In the above embodiment, C, M, and Y, which are primary colors of the CMYK color system, and R, G, and B, which are secondary colors, are designated as specific colors, and are subject to correction in S640 and S650 in FIG. It is said that The corrections in S640 and S650 may be performed on primary colors and secondary colors of other color systems without being limited to this. For example, when the reading execution unit 290 of the MFP 200 prints using light cyan (LC) and light magenta (LM) in addition to C, M, and Y, C, M, Y, LC, and LM A printed image is represented by a color system of five components. In this case, for example, in addition to the C, M, and Y primary colors, the primary colors expressed using only the LC printing material or the primary colors expressed using only the LM printing material may be corrected. .

(2) In the above embodiment, the CMYK values are corrected after converting the RGB values of the pixels in the specific color area into CMYK values. Alternatively, for example, the RGB values of each pixel within the specific color area may be corrected. In this case, correction for reducing the G value and the B value is performed for the specific color region of R, which is the primary color of the RGB color system. For example, corrected RGB values are converted to CMYK values. Further, correction for reducing the R value and the B value is performed for the G specific color area. Correction to reduce the R value and the G value is performed on the specific color region of B. FIG. Further, a correction to reduce the R value is performed on a specific color region of C, which is a secondary color in the RGB color system. Correction to reduce the G value is performed on the M specific color region, and correction to reduce the B value is performed on the Y specific color region. Alternatively, after correcting the RGB values of each pixel in the specific color region, an image represented by the corrected RGB values may be output by, for example, a display unit without color conversion to CMYK values. good.

(3) In the above embodiment, it is determined for each block BL whether or not it is a specific color area, and if it is a specific color area, the pixel values within the specific color area are corrected. Alternatively, it may be determined whether each pixel has a specific color, and if it has a specific color, the value of the pixel may be corrected.

(4) In the above embodiment, the character area and the non-character area are identified (S20 in FIG. 2), and after the non-character area is smoothed (S30 in FIG. 2), the specific color Area identification and correction for the specific color area (S45 in FIG. 2) are executed. Alternatively, the specific color area may be specified and the correction for the specific color area may be performed after the smoothing process is performed on the entire area without specifying the character area and the non-character area. Alternatively, the specific color area may be specified and the correction for the specific color area may be performed without specifying the character area and performing the smoothing process.

(5) In the above embodiment, the condition for determining a single area as a specific color area is that the saturation and brightness are equal to or higher than the reference (S530, S535 in FIG. 8). Alternatively, a single region may be determined as the specific color region based only on hue, or a single region may be determined as the specific color region based on only lightness and hue, or only saturation and hue. can be

(6) In the above embodiment, in S50 of FIG. 2, print data is generated using processed image data after color conversion processing. Alternatively, image data for storage (for example, a PDF file) may be generated using processed image data after color conversion processing.

(7) The image processing apparatus that implements the image processing in FIG. 2 is not limited to the multifunction machine 200, and may be various apparatuses. For example, the image processing of FIG. 2 may be executed by a scanner or digital camera to generate print data to be supplied to a printer using target image data generated by itself. Further, for example, a terminal device (for example, the terminal device 100) or a server (not shown) connected to be able to communicate with a scanner or printer executes the image processing of FIG. 2 using the scan data acquired from the scanner. may generate print data and supply the print data to the printer. Also, a plurality of computers (for example, a cloud server) that can communicate with each other via a network may share functions required for image processing, and execute image processing as a whole. In this case, the entirety of a plurality of computers is an example of an image processing device.

(8) In each of the above embodiments, part of the configuration implemented by hardware may be replaced with software, or conversely, part or all of the configuration implemented by software may be replaced with hardware. You may do so. For example, a part of the image processing in FIG. 2 (for example, color conversion processing for non-specific color areas in S230 in FIG. 5) may be executed by dedicated hardware such as ASIC.

Although the present invention has been described above based on examples and modifications, the above-described embodiments of the present invention are intended to facilitate understanding of the present invention, and are not intended to limit the present invention. The present invention may be modified and modified without departing from the spirit and scope of the claims, and the present invention includes equivalents thereof.

DESCRIPTION OF SYMBOLS 100... Terminal device, 200... MFP, 210... CPU, 220... Volatile memory device, 230... Non-volatile memory device, 240... Display part, 250... Operation part, 270... Communication IF, 280... Print execution part, 290 ... reading execution part, PG ... computer program, CT ... color conversion table

Claims (9)

An image processing device,
an image acquisition unit that acquires target image data generated using an image sensor;
A first area identifying unit that uses the target image data to identify a specific color area within a target image based on the target image data, wherein the specific color area is three or more colors of a specific color system. a region to be represented by a specific color that is either a primary color that is a color corresponding to one of the components or a secondary color that is a color corresponding to two components; a region identifying part of
A correcting unit that performs correction for reducing a specific component on values of a plurality of pixels in the specified specific color region to generate corrected image data, wherein the specific component is the specific color. The correction unit, which is a component different from the corresponding component;
a second region specifying unit that specifies a character region representing a character and a non-character region representing an image different from the character in the target image using the target image data;
a smoothing processing unit that performs smoothing processing on the non-character area;
with
The image processing device , wherein the correcting unit uses the smoothed target image data to perform correction for reducing the specific component on the smoothed portion . The image processing device according to claim 1,
The first area specifying unit further uses the target image data to specify a non-specific color area that is different from the specific color area in the target image,
The correcting unit performs correction to reduce the specific component with respect to the values of the plurality of pixels in the specified color area, and the values of the plurality of pixels in the specified non-specific color area. image processing device that does not perform correction to reduce the specific component for The image processing device according to claim 1 or 2 , further comprising:
a generation unit that uses the corrected image data to generate print data for executing printing using a plurality of color materials;
The image processing device, wherein the correction for reducing the specific component is correction for reducing the amount of use of at least one colorant among the plurality of colorants. The image processing device according to claim 3 ,
The image processing apparatus, wherein the target image data is image data indicating a color for each pixel with a color value of a first color system including components corresponding to a plurality of signals output from the image sensor. The image processing device according to claim 4 , further comprising:
converting a value of each pixel of the target image data from a color value of the first color system to a color value of a second color system having a plurality of components corresponding to the plurality of color materials; a color conversion unit for generating converted image data,
The correcting unit performs correction to reduce the specific component with respect to the color values of the plurality of pixels in the specific color area in the converted image data in the second color system, thereby performing the correction. An image processing device that generates processed image data. The image processing device according to any one of claims 1 to 5 ,
The first region specifying unit determines whether each of a plurality of blocks set in the target image is the specific color region using values of a plurality of pixels in the block. An image processing device that identifies the specific color region by making a determination. The image processing device according to any one of claims 1 to 6 ,
The image processing device, wherein the first region specifying unit specifies the specific color region using the smoothed target image data. The image processing device according to any one of claims 1 to 7 ,
wherein the specific color region is a region represented by a color whose hue corresponds to one of the primary color and the secondary color and whose saturation and lightness are equal to or higher than a reference; Device. A computer program,
an image acquisition function for acquiring target image data generated using an image sensor;
A first region specifying function for specifying a specific color region in a target image based on the target image data using the target image data, wherein the specific color region is three or more colors of a specific color system. a region to be represented by a specific color that is either a primary color that is a color corresponding to one of the components or a secondary color that is a color corresponding to two components; and the region-specific function of
A correction function for generating corrected image data by performing correction for reducing a specific component on values of a plurality of pixels in the specified specific color region, wherein the specific component is the specific color. The correction function, which is a component different from the corresponding component;
a second region specifying function for specifying a character region representing a character and a non-character region representing an image different from the character in the target image using the target image data;
a smoothing processing function that performs smoothing processing on the non-character area;
is realized on a computer ,
A computer program according to claim 1, wherein the correction function uses the smoothed target image data to perform correction for reducing the specific component on the smoothed portion .

Images