JP2020136785A - Image processing apparatus, and computer program - Google Patents

Abstract

To provide an image processing apparatus that suppresses the lowering of image quality, which originates in reading characteristics of an image sensor.SOLUTION: The image processing apparatus includes: an image acquisition unit that acquires target image data which is generated by use of an image sensor; a region specifying unit that specifies a specific color region in a target image which is indicated by the target image data, by use of the target image data; and a correction unit that generates corrected image data by executing a correction of reducing a specific component for values of a plurality of pixels in the specific color region which has been already specified. The specific color region is a region that should be represented by a specific color which is any one of a primary color that is a color corresponding to one component among three or more components in a specific color system, and a secondary color that is a color corresponding to two components. The specific component is a component different from the component corresponding to the specific color.SELECTED DRAWING: Figure 9

Description

The present specification relates to image processing using image data generated by 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 correction color of C is, for example, a color in which the two components G and B take the maximum value (255).

JP-A-2009-159284

By the way, when the image generated by the image sensor has a color component different from the color on the document due to the reading characteristics such as the relationship between the halftone dots on the document and the reading position of the image sensor. There is. Such a color component may reduce the image quality of the image.

The present specification discloses a technique capable of suppressing deterioration of image quality due to the reading characteristics of an image sensor when image data generated by using an image sensor is used.

The techniques disclosed herein have been made to solve at least a portion of the above problems and can be realized as the following application examples.

[Application Example 1] An image processing apparatus in an image acquisition unit that acquires target image data generated by using an image sensor, and a target image indicated by the target image data using the target image data. A region specifying portion that specifies a specific color region, and the specific color region corresponds to a primary color and two components that are colors corresponding to one component out of three or more components of a specific color system. The value of the first region specifying portion, which is a region to be represented by a specific color, which is one of the secondary colors, and the values of a plurality of pixels in the specified specific color region. A correction unit that generates corrected image data by executing a correction for reducing a specific component, 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, if a component different from the component corresponding to the primary color or the secondary color is mixed in the specific color region, the image quality may deteriorate. According to the above configuration, the values of a plurality of pixels in the specific color region are corrected to reduce specific components different from the corresponding components, so that the specific components are mixed with the primary color and the secondary color. It is possible to suppress the deterioration of the image quality. As a result, when the image data generated by the image sensor is used, it is possible to suppress the deterioration of the image quality due to the reading characteristics of the image sensor.

The techniques disclosed in the present specification can be realized in various forms, for example, a printing device, a printing method, an image processing method, a function of these devices, or a computer program for realizing the above method. , A recording medium on which the computer program is recorded, and the like can be realized.

It is a block diagram which shows the structure of the multifunction device 200 which is an example of an image processing apparatus. It is a flowchart of image processing. It is the first figure which shows an example of the image used in the image processing. It is a flowchart of character identification processing. It is a flowchart of a color conversion process. It is a flowchart of a single judgment process. It is explanatory drawing of the single determination process. It is a flowchart of primary color / secondary color determination processing. It is a flowchart of a color conversion process for a specific color area. It is a figure which shows an example of the tone curve used for correction. It is a conceptual diagram of the area of the primary color of a manuscript.

A. Example:
A-1. Configuration of the multifunction device 200 An embodiment will be described based on an embodiment. FIG. 1 is a block diagram showing a configuration of a multifunction device 200, which is an example of an image processing device. The multifunction device 200 includes a CPU 210 which is a processor for controlling an image processing device, a volatile storage device 220 such as a DRAM, a non-volatile storage device 230 such as a flash memory and a hard disk drive, and 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 a user's terminal device 100, a print execution unit 280, and a reading execution unit 290. , Is equipped.

The scanning execution unit 290 generates scan data by optically scanning the document using the one-dimensional image sensor under the control of the CPU 210. According to the control of the CPU 210, the print execution unit 280 uses a plurality of types of toners, specifically, cyan (C), magenta (M), yellow (Y), and black (K) toners as coloring materials. The image is printed on a printing medium such as paper by the laser method. Specifically, the print execution unit 280 exposes the photosensitive drum to form an electrostatic latent image, and attaches 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 to the paper. In the modified example, the print execution unit 280 may be an inkjet type print execution unit that ejects ink as a coloring material to form an image on paper.

The volatile storage device 220 provides a buffer area for temporarily storing various intermediate data generated when the CPU 210 performs processing. The non-volatile storage device 230 stores a computer program PG and a color conversion table CT. The computer program PG is a control program that allows the CPU 210 to control the multifunction device 200. In this embodiment, the computer program PG and the color conversion table CT are provided in a form stored in advance in the non-volatile storage device 230 at the time of manufacturing the multifunction device 200. Instead, the computer program PG and the color conversion table CT may be provided in a form downloaded from a server, or may be provided in a form stored in a DVD-ROM or the like. The CPU 210 can execute image processing 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 the document on the platen of the scanning execution unit 290 and inputs a copy execution instruction. In this image processing, scan data generated by scanning a document using a scanning execution unit 290 is acquired, and the scan data is used to generate print data indicating the document, thereby producing a so-called copy of the document. It is a process to be realized.

In S10, the CPU 210 generates scan data by reading the document placed on the platen by the user using the scanning execution unit 290. The manuscript is, for example, a printed matter in which an image is printed by a multifunction device 200 or a printer (not shown). The generated scan data is stored in the buffer area of the volatile storage device 220 (FIG. 1). The scan data includes the values of a plurality of pixels, and each of the values of the plurality of pixels represents the color of the pixel as an RGB color value (also referred to as an RGB value). That is, the scan data is RGB image data. The RGB value of one pixel includes, for example, the values of three color components (hereinafter, also referred to as R value, G value, and B value) of red (R), green (G), and blue (B). I'm out. In this embodiment, the number of gradations of each component value is 256 gradations.

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

The scan data, which is RGB image data, includes three component image data (R component image data, G component image data, B component image data) corresponding to the three color components constituting the RGB color system. Can be said. Each component image data is image data in which the value of one type 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 the scanned image SI represented by the scan data. The scanned 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 shows a white background Bg1 indicating the background color of the paper of the original, objects Ob1 to Ob3 different from the three characters, four characters Ob4 to Ob7, and four characters. The background Bg2 of the characters Ob4 to Ob7 and the like are included. Objects that are different from text are, for example, photographs. The 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 indicating characters and a plurality of non-character pixels indicating no 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) in which the value of the character pixel is set to "1" and the value of the non-character pixel is set to "0" is generated. FIG. 3B shows an example of the character identification image TI represented by the character identification data. In this character identification image TI, a plurality of pixels constituting the edges of the four characters Ob4 to Ob7 in the scanned image SI are specified as character pixels Tp4 to Tp7. For relatively large characters, the pixels constituting the edges of the characters are specified as character pixels, and for relatively small characters, the entire pixels constituting the characters are specified as character pixels. The character identification process will be described later.

In S30, the CPU 210 executes a halftone dot smoothing process on the scan data to generate smoothed image data indicating the smoothed image. Specifically, the CPU 210 executes a smoothing process 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 the smoothing process has been completed. Calculate the values of a plurality of non-character pixels. The non-character pixel to be smoothed is specified by the character identification process of S20. The CPU 210 generates smoothed image data including the values of the plurality of character pixels included in the scan data and the values of the plurality of non-character pixels that have been smoothed.

FIG. 3C shows a 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 with a smoothed background Bg2, and a background Bg2g. Of these objects Ob1g to Ob7g and background Bg2g, parts other than the characters Ob4g to Ob7g (also referred to as non-character parts) are smoothed as compared with the scanned image SI.

In S40, the CPU 210 executes character sharpening processing on the smoothed image data to generate the processed image data. Specifically, the CPU 210 executes a sharpening process such as an unsharp mask process or a process of applying a sharpening filter on each of the values of a plurality of character pixels included in the smoothed image data. Calculate the values of a plurality of character pixels that have been sharpened. The character pixel to be sharpened is specified by the character identification process of S20. Then, the CPU 210 includes the values of the plurality of non-character pixels included in the smoothed image data (values of the plurality of non-character pixels that have been smoothed) and the values of the plurality of character pixels that have been sharpened. Generates processed image data including ,. Since the values of the plurality of character pixels included in the smoothed image data are not subject to the smoothing process, they are the same as the values of the plurality of character pixels included in the scan data. Therefore, it can be said that the character sharpening process of this step is executed for the values of a plurality of character pixels included in the scan data.

FIG. 3D shows the processed image FI represented 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. Of these objects Ob1f to Ob7f and the background Bg2f, the edges of the characters Ob4f to Ob7f are sharpened as compared with the characters Ob4 to Ob7 in the scanned image SI and the characters Ob4g to Ob7g in the smoothed image GI. There is. Further, the objects Ob1f to Ob3f and the background Bg2f other than the characters 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 a color conversion process on the processed image data. The color conversion process is a process of converting processed image data, which is RGB image data, into CMYK image data. The CMYK image data is image data indicating a color for each pixel by a CMYK value. The CMYK value is a color value of a color system (also referred to as a CMYK color system) having a plurality of components (components of C, M, Y, and K) corresponding to a plurality of color materials used for printing. The color conversion process of this embodiment includes a correction process for removing color turbidity in a region showing six colors C, M, Y, R, G, and B. This color conversion process will be described later.

In S50, the CPU 210 executes a print data generation process including a halftone process on the processed image data after the color conversion process. Specifically, halftone processing is executed on the processed image data (CMYK image data) after the color conversion processing, and the dot formation state is determined for each color material used for printing and for each pixel. The indicated dot data is generated. The dot formation state can be, for example, two types of states with or without dots, and four types of states of large dots, medium dots, small dots, and no dots. The halftone processing is performed according to, for example, a dither method or an error diffusion method. The dot data are rearranged in the order in which they are used 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 the print process and ends the image process. Specifically, the CPU 210 supplies print data to the print execution unit 280, and causes the print execution unit 280 to print the processed image.

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

More specifically, processed image data including the values of the plurality of character pixels that have been sharpened and the values of the plurality of non-character pixels that have been smoothed is generated (S30, S40). .. As a result, it is possible to generate processed image data showing the processed image FI that looks good.

For example, as shown in the processed image FI of FIG. 3D, in the processed image data, the sharpening-processed value is used as the value of the character pixel. As a result, the characters of the processed image FI look sharp, so that the appearance of the processed image FI to be printed can be improved, for example.

Further, in the processed image data, the smoothed value is used as the value of the background Bg2 in the processed image FI and the non-character pixel constituting an object different from the character such as a photograph. As a result, it is possible to prevent, for example, halftone dots that cause moire from appearing in a portion different from the characters of the processed image FI, so that it is possible to prevent problems such as moire from occurring in the printed processed image FI. it can. As a result, the appearance of the processed image FI to be printed can be improved.

For example, the original used to generate the scan data is a printed matter on which an image is printed. For this reason, for example, a uniform portion such as a background Bg2 having a color different from white in a document forms halftone dots when viewed at the dot level forming an image. The halftone dots include a plurality of dots and a portion where the dots are not arranged (a portion indicating the ground color of the document). For this reason, halftone dots are shown in the region showing the background Bg2 in the scanned image SI when viewed at the pixel level. The dots in the halftone dots are arranged with periodicity due to the influence of the dither matrix used when printing the original. For this reason, when printing is performed using scan data, the periodic components of halftone dot dots existing in the original image (scan image SI) before halftone processing and the dots of halftone dots that make up the printed image Moire is likely to appear due to interference with the periodic component. In the processed image FI of this embodiment, the smoothing process reduces the periodic component of dots in a portion different from the edge in the original image (scanned image SI). As a result, when the processed image FI is printed using the processed image data, for example, it is possible to suppress the occurrence of moire in the printed processed image FI.

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

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

In S220, the CPU 210 executes an edge extraction process 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 strength Se are set as the values of a plurality of pixels.

In S240, the CPU 210 executes a binarization process on the edge extraction data. As a result, binary image data as character specific data is generated. For example, in the edge extraction data, the CPU 210 classifies pixels having a pixel value (that is, edge strength) equal to or more than a threshold value as character pixels, and classifies pixels having a pixel value less than the threshold value into non-character pixels. .. In the binary image data, as described above, the value of the character pixel is set to "1", and the value of the non-edge pixel is set to "0".

In the character identification data described above, edges such as graphic lines different from the characters can also be specified as character pixels. In this case, an edge different from the character is targeted for the character sharpening process of S40, but there is no problem because the line such as a graphic has a sharp appearance. As a modification, another character identification process devised to specify the character area (character pixel) with higher accuracy may be adopted. For example, the CPU 210 specifies an area where the edge strength is larger than the reference value, and specifies the area as an object area. The CPU 210 determines whether the object in each object area is a character or an object other than the character (for example, a photograph or drawing). The type of object is determined based on the color distribution of each object area (for example, the number of colors that the object has), the ratio of the pixels that make up the object to the total number of pixels in the rectangle that circumscribes the object, and so on. It is said. Various known methods can be adopted as the method for specifying such a character region, and the known methods are disclosed in, for example, JP-A-5-225378 and JP-A-2002-288589. ..

A-4. Color conversion process The color conversion process of S45 in FIG. 2 will be described. FIG. 5 is a flowchart of the color conversion process. In S300, the CPU 210 causes the CPU 210 to execute a single determination process on the processed image data. The single determination process divides the processed image FI into a plurality of block BLs, and determines whether each block BL is a single region representing a single color or a non-single region representing a plurality of colors. It is a judgment process. Since the processed image data is image data based on the scan data, the processed image FI contains color unevenness due to blurring during reading and the like. A single area is not limited to an area composed of pixels having 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 or the like. Includes area.

FIG. 6 is a flowchart of the single determination process. 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 block BLs. 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 block BLs arranged in a matrix. One block BL contains, for example, m × n (m × n) pixels (m and n are integers of 2 or more). For example, m and n are 5.

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

In S430, the CPU 210 classifies the plurality of pixels in the block of interest into any 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 RGB color space CP of the legislation 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), and 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 consists of eight cubic spaces CSc, CSm, CSy, which correspond to eight basic colors C, M, Y, R, G, B, K, and W by three planes F1 to F3. 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. The 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 space CSc hatched in FIG. 7 (B) is a space corresponding to cyan (C). For example, among the plurality of pixels in the block of interest, the pixels located in the space CSc whose corresponding RGB values in the scanned image SI correspond to C are classified as C.

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

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

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

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

At this point, all blocks in the processed image FI are determined to be either single area or non-single area. For example, the blocks BLa and BLc in FIG. 7A include an image showing a uniform background BG1f and Bg2f. For this reason, these blocks BLa, BLc are determined as a single region. Further, the block BLb includes the character Ob4f and the background Bg2f. For this, the block BLb is determined as a non-single region. Although not shown, for example, when the objects Ob1f to Ob3f of the processed image FI include a portion representing a single color, the block BL indicating the portion is determined as a single region. Further, since the edge portions of the objects Ob1f to Ob3f show a plurality of colors, the block BL showing the edge portions is determined as a non-single region.

In S310 of FIG. 5, the CPU 210 executes the primary color / secondary color determination process. The primary color / secondary color determination process is a process for determining whether or not the single area is a specific color area for each of the plurality of single areas determined by the single determination process. The specific color region includes a primary color region and a secondary color region. The primary color region is a region to be represented by a color (also referred to as a primary color) corresponding to one of the C, M, and Y components of the CMYK color system. The primary color region includes a cyan region to be represented by cyan, a magenta region to be represented by magenta, and a magenta region to be represented by yellow. The secondary color region is a region to be represented by a color (also referred to as a secondary color) corresponding to two of the C, M, and Y components. In this embodiment, the secondary colors are blue (B), which is a color corresponding to C and M, red (R), which is a color corresponding to M and Y, and a color corresponding to Y and C. There are three colors, green (G). Therefore, the secondary color region includes a blue region to be represented by blue, a red region to be represented by red, and a green region to be represented by green. It can also be said that the secondary color corresponding to the two components is a color obtained by mixing the two primary colors corresponding to the two components.

FIG. 8 is a flowchart of the primary color / secondary color determination process. In S510, the CPU 210 selects one unit of interest from a 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, the average color value (R_av, G_av, B_av) of the values (RGB values) of all the pixels in the single region of interest is calculated.

In S525, the CPU 210 converts the average color value (RGB value) of the single region of interest into the color value (also referred to as HSV value) of the HSV color system by using a known conversion formula. Each HSV value includes three component values, that is, an H value indicating hue, an S value indicating saturation, and a V value indicating lightness. The H value takes a value in the range of 0 degrees to 360 degrees. The S value takes a value in the range of 0% to 100%, and 0% indicates an achromatic color. The V value takes a value of 0% to 100%, 0% indicates black, and 100% indicates white.

In S530, the CPU 210 determines whether or not the saturation (S value) of the average color value (HSV value) after conversion is equal to or greater than the threshold value THs. The threshold THs is, for example, 50%. When the saturation (S value) is less than the threshold value THs (S530: NO), in S550, the CPU 210 determines the single region of interest as the non-specific color region.

When the saturation (S value) is equal to or higher than the threshold value THs (S530: YES), in S535, the CPU 210 has the brightness (V value) of the average color value (HSV value) after conversion equal to or higher than the threshold value THv. Judge whether or not. The threshold THv is, for example, 50%. When the brightness (V value) is less than the threshold value THv (S535: NO), in S550, the CPU 210 determines the single area of interest as the non-specific color area.

When the brightness (V value) is equal to or higher than the threshold value THv (S535: YES), in S540, the CPU 210 determines that the hue (H value) of the converted average color value (HSV value) is within the predetermined primary color range. , Or, it is determined whether or not it is within the predetermined secondary color range. The primary color range is, for example, a range corresponding to the hues of C, M, and Y among the values in the range of 0 degrees to 360 degrees. The secondary color range is a range corresponding to the hues of R, G, and B among the values in the range of 0 degrees to 360 degrees. For example, the primary color range of C, M, and Y is a range of a predetermined width (for example, 30 degrees) centered on 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) centered on the H value indicating the hue of R, G, and B, respectively.

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

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

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

At this point, all single regions in the processed image FI are determined to be either specific color regions or non-specific color regions. For example, when the block BLa in FIG. 7 (A) is a single region, but the background Bg2f is a uniform background of primary colors or secondary colors, the block BLa is determined as a specific color region. Further, since the block BLc is a single region but the background Bg1f is white, it is determined as a non-specific color region. Although not shown, for example, when a part of the objects Ob1f to Ob3f of the processed image FI is determined as a single region, the single region includes a specific color region and a non-specific color region. It is decided to be one of.

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

In S320 of FIG. 5, the CPU 210 executes a color conversion process for a non-specific color region in the processed image FI. Specifically, the values (RGB values) of the plurality of pixels included in the non-specific color region are converted into CMYK values one by one with reference to the color conversion table CT (FIG. 1). The color conversion table CT is, for example, a known look-up table that defines the correspondence between RGB values and CMYK values.

In S330, the CPU 210 executes a color conversion process for a specific color region of the processed image FI. The color conversion process for a specific color area is different from the color conversion process for a non-specific color area. FIG. 9 is a flowchart of a color conversion process for a specific color region.

In S610, the CPU 210 selects one specific color region of interest from the plurality of specific color regions determined by the primary color / secondary color determination process of S310 in FIG.

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

In S620, the CPU 210 converts the values of all the pixels in the specific color region of interest into one corresponding CMYK value. Therefore, the converted value of all the pixels in the specific color region of interest is one CMYK value common to all the pixels.

In S630, the CPU 210 determines whether the specific color region of interest is the primary color region or the secondary color region. For example, when the H value of the average color value (HSV value) of the specific color region of interest is in the primary color range, it is determined that the specific color region of interest is in the primary color region, and the H value is in the secondary color range. It is determined that the specific color region of interest is a secondary color region. When the specific color region of interest is the primary color region (S630: YES), in S640, the CPU 210 sets the component corresponding to the primary color among the four component values of the CMYK values of each pixel of the specific color region of interest. Corrects three different component values. For example, when the specific color region of interest is the cyan (C) region, the three component values (that is, M value, Y value, and K value) excluding the C value are the targets of correction. Similarly, when the specific color region of interest is the magenta (M) region, the C value, Y value, and K value are the targets of correction. When the specific color region of interest is the yellow (Y) region, the C value, M value, and K value are the targets of 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 component value to be corrected for 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 the correction. Specifically, all the component values to be corrected below the threshold value Vth (for example, 10) are converted to the minimum value (0). The component value to be corrected having a threshold value Vth or more and less than 255 is converted into a smaller value than before the 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 value in the primary color region, each component value to be corrected is a relatively small value. It is considered that it is not the maximum value.

When the attention specific color region is the secondary color region (S630: NO), in S650, the CPU 210 sets the secondary color among the four component values of the CMYK values of each pixel of the attention specific color region. Two component values that are different from the corresponding components are corrected. For example, when the non-specific color region is the blue (B) region, the two component values (that is, the Y value and the K value) excluding the C value and the M value are the targets of correction. Similarly, when the specific color region of interest is the red (R) region, the C value and the K value excluding the M value and the Y value are the targets of correction. When the specific color region of interest is the green (G) region, the M value and the K value excluding the Y value and the C value are the targets of correction.

The CPU 210 applies the tone curve of FIG. 10 to each component value to be corrected for 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 the correction.

In S660, the CPU 210 determines whether or not all the specific color regions in the processed image FI have been processed as the specific color regions of interest. If there is an unprocessed specific color region (S660: NO), the CPU 210 returns the processing to S610. When all the specific color regions have been processed (S660: YES), the CPU 210 ends the color conversion processing for the specific color regions.

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

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

Due to the reading characteristics of the image sensor, if a component different from the component corresponding to the primary color or the secondary color is mixed in the specific color region, the image quality may deteriorate. According to this embodiment, since the correction for reducing the specific component is executed for the values of a plurality of pixels in the specific color region such as the primary color and the secondary color, the specific component is mixed with the primary color and the secondary color. Therefore, it is possible to suppress the deterioration of the image quality. As a result, when the image data generated by the image sensor is used, it is possible to suppress the deterioration of the image quality due to the reading characteristics of the image sensor.

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

In addition, the manuscript is generally a printed matter in many cases. The scan data obtained by reading the Y region of a printed document tends to include RGB values including a B value larger than 0. FIG. 11 is a conceptual diagram of the primary color region of the document. The Y region YA of the original printed matter is a uniform Y region from a macroscopic viewpoint, but as shown in FIG. 11, it is a halftone dot region from a microscopic viewpoint. The halftone dot region is a region in which a region in which a Y dot DT formed of a coloring material such as ink or toner is formed and a white region WA in which the ground color (white) of the paper is exposed are mixed. Therefore, when the image sensor reads the Y dot DT, the B value of the generated RGB value becomes almost 0, but when the image sensor reads the white area WA, the generated RGB value B is The value will be greater than 0. Due to the reading characteristics of such an image sensor, the area Y on the document is read, even though the area Y should be represented by Y when output by printing, for example. In the scan data, a B value larger than 0 is mixed with the RGB value in the Y region. Due to this B value, for example, in an image indicated by scan data or an image printed using the scan data, turbidity may occur in a region to be represented by Y. Such inconvenience is not limited to the Y region, but is the region of other primary colors of the CMYK color system such as cyan (C) and magenta (M) and the secondary color region of the CMYK color system such as R, G, and B. But it happens. According to this embodiment, it is possible to reduce the turbidity of such primary colors and secondary colors and suppress the deterioration of image quality.

Further, in the above embodiment, a process of generating print data for executing printing using the CMYK color material using the corrected image data (processed image data after the color conversion process) (S50 in FIG. 2). ) Is an example of the generation unit. The correction for reducing the specific component is a correction for reducing the specific component corresponding to the CMYK color material (FIG. 10). Therefore, it can be said that the correction is a correction that reduces the amount of at least one of the CMYK color materials used. As a result, when printing is performed using the 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, the primary color of the CMYK color system (for example, C) is a color to be printed using only one kind of color material (for example, the color material of C) corresponding to the primary color. For this reason, when a small amount of color material (for example, M, Y, K color material) different from the color material corresponding to the primary color is mixed in the primary color region in the printed image, for example, three or more kinds of color materials are used. Compared with the color region to be printed using the coloring material, the color turbidity due to the small amount of the coloring material is more noticeable. Similarly, the secondary color of the CMYK color system (for example, B) is a color to be printed using only two kinds of color materials (for example, C and M) corresponding to the secondary color. For this reason, when a small amount of color material (for example, Y, K color material) different from the color material corresponding to the secondary color is mixed in the secondary color region in the printed image, for example, three types are used. Compared with the color region to be printed using the above color material, the color turbidity due to the small amount of the color material is more noticeable. According to this embodiment, since the turbidity of the primary color and the secondary color in which the turbidity of the color is conspicuous can be appropriately reduced, the deterioration of the image quality of the printed image can be effectively suppressed.

Further, according to the above embodiment, the scan data as the target image data is RGB image data indicating the color of each pixel by the color value (RGB value) 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 a secondary color of the RGB color system. Further, 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 the present embodiment, among the three component values of RGB, the component value that should ideally be 0 becomes a value larger than 0 due to the reading characteristics of the image sensor. In addition, since the correction is performed so as to reduce the component value that should be 0, it is possible to effectively suppress the deterioration of the image quality of the image based on the scan data.

Further, according to the above embodiment, the CPU 210 as the color conversion unit executes a color conversion process for converting the pixel values of the processed image data from RGB values to CMYK values (S320 in FIG. 5 and S620 in FIG. 9). ). The CPU 210 as a correction unit executes correction for reducing the specific components with respect to the CMYK values of a plurality of pixels in the specific color region (S640, 650 in FIG. 9). As a result, for example, the amount of the color material corresponding to the specific component can be appropriately reduced as compared with the case where the RGB value is corrected. Therefore, it is possible to effectively suppress the deterioration of the image quality of the printed image due to the mixing of a small amount of the color material corresponding to the specific component.

Further, according to the above embodiment, the CPU 210 as the first region specifying unit uses the values of the plurality of pixels in the block BL for each of the plurality of block BLs set in the processed image FI. It is determined whether or not the block BL is a specific color region (S300, S310, FIG. 6, FIG. 8 in FIG. 5). As a result, a specific color region is specified. As a result, a specific color region in the processed image FI can be appropriately specified.

Further, in the above embodiment, the CPU 210 that executes the process of specifying the character area and the non-character area in the scanned image SI using the scan data (S20 in FIG. 2) is an example of the second area specifying unit. .. The CPU 210 that executes the process of executing the smoothing process on the non-character area (S30 in FIG. 2) is an example of the smoothing process unit. The CPU 210 identifies a specific color region using the smoothed scan data (that is, the processed image data) (S300 and 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 should be specified more appropriately than when the scan data before the smoothing process is used. Can be done.

For example, as described above, the halftone dot region is uniform from the macro viewpoint, but is non-uniform because the dot (halftone dot) color and the background color of the paper are mixed from the micro viewpoint. 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 the macro viewpoint. In this case, from a macro perspective, it may not be possible to appropriately determine a uniform primary color or secondary color region as a specific color region. According to this embodiment, since the smoothed scan data is used, the single determination process is performed after the halftone dot region is made uniform even from the microscopic viewpoint. As a result, from a macro perspective, a uniform primary color or secondary color region can be appropriately determined as a specific color region.

Further, according to the above embodiment, the CPU 210 uses the smoothed scan data to perform a correction for reducing a specific component (S330 in FIG. 4). As a result, the image quality of the corrected specific color region can be improved as compared with the case of using the scan data before the smoothing process. For example, in the image to be corrected, when there is a relatively wide portion of a specific color (primary color or secondary color), a plurality of specific color regions of the same color are continuously arranged in the portion. If there is color unevenness in the portion, a color difference 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 deteriorate. According to this embodiment, by performing the smoothing process on the scan data, it is possible to reduce the color unevenness that occurs in the portion where the specific color regions are continuously arranged. Therefore, by performing a correction that reduces a 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.

Further, according to the above embodiment, the specific color region is a range in which the hue (for example, H value) corresponds to either the primary color or the secondary color, and the saturation (for example, S value) and the lightness. It is a region represented by a color in which (for example, V value) is equal to or higher than the reference value (S530 to S550 in FIG. 8). In the region where the saturation and lightness are above the standard, when a specific component is mixed with the primary color or the secondary color, the deterioration of the image quality is more noticeable than in the region where the saturation and the lightness are below the standard. According to the above configuration, it is possible to effectively suppress the deterioration of the image quality in the specific color region where the deterioration of the image quality is 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 Example (1) In the above embodiment, the primary colors C, M, and Y of the CMYK color system and the secondary colors R, G, and B are set as specific colors, and the correction targets of S640 and S650 in FIG. It is said that. Not limited to this, other primary colors and secondary colors of the color system may be subject to correction of S640 and S650. For example, when the reading execution unit 290 of the multifunction device 200 prints using light cyan (LC) or light magenta (LM) in addition to C, M, Y, C, M, Y, LC, LM. The printed image is expressed by the color system of the five components of. In this case, for example, in addition to the primary colors C, M, and Y, 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 RGB values of the pixels in the specific color region are converted into CMYK values, and then the CMYK values are corrected. Instead of this, for example, the RGB value of each pixel in the specific color region may be corrected. In this case, a correction for reducing the G value and the B value is executed for the specific color region of R, which is the primary color of the RGB color system. For example, the corrected RGB value is converted into a CMYK value. Further, a correction for reducing the R value and the B value is executed for the specific color region of G. A correction for reducing the R value and the G value is executed for the specific color region of B. Further, a correction for reducing the R value is executed for a specific color region of C, which is a secondary color of the RGB color system. A correction for reducing the G value is executed for the specific color region of M, and a correction for reducing the B value is executed for the specific color region of Y. Further, even if the RGB value of each pixel in the specific color region is corrected and then the image represented by the corrected RGB value is output by the display unit without color conversion to the CMYK value. Good.

(3) In the above embodiment, it is determined for each block BL whether or not it is a specific color region, and if it is a specific color region, the values of the pixels in the specific color region are corrected. Instead of this, it is determined whether or not 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 specified (S20 in FIG. 2), the smoothing process is executed for the non-character area (S30 in FIG. 2), and then the specific color is specified. The region is specified and the correction for the specific color region (S45 in FIG. 2) is executed. Instead of this, the specific color area may be specified and the correction for the specific color area may be executed after the smoothing process is executed as a whole without specifying the character area and the non-character area. Further, the specific color area may be specified and the correction for the specific color area may be executed without specifying the character area or performing the smoothing process.

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

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

(7) The image processing device that realizes the image processing of FIG. 2 is not limited to the multifunction device 200, and may be various devices. For example, the scanner or the digital camera may execute the image processing of FIG. 2 in order to generate print data to be supplied to the printer by using the target image data generated by the scanner or the digital camera. Further, for example, a connected terminal device (for example, terminal device 100) or a server (not shown) capable of communicating with the scanner or printer executes the image processing of FIG. 2 using the scan data acquired from the scanner. , Print data may be generated and the print data may be supplied to the printer. Further, a plurality of computers (for example, a cloud server) capable of communicating with each other via a network may partially share the functions required for image processing and execute image processing as a whole. In this case, the entire plurality of computers is an example of an image processing device.

(8) In each of the above embodiments, a part of the configuration realized by the hardware may be replaced with software, and conversely, a part or all of the configuration realized by the software may be replaced with the hardware. You may do so. For example, a part of the image processing of FIG. 2 (for example, the color conversion processing for the non-specific color region of S230 of 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 invention are for facilitating the understanding of the present invention and do not limit the present invention. The present invention can be modified and improved without departing from the spirit and claims, and the present invention includes equivalents thereof.

100 ... Terminal device, 200 ... Multifunction device, 210 ... CPU, 220 ... Volatile storage device, 230 ... Non-volatile storage device, 240 ... Display unit, 250 ... Operation unit, 270 ... Communication IF, 280 ... Print execution unit, 290 … Reading execution unit, PG… Computer program, CT… Color conversion table

Claims (9)

It is an image processing device
An image acquisition unit that acquires target image data generated using an image sensor,
It is a first area specifying part which specifies a specific color region in a target image based on the target image data using the target image data, and the specific color region is three or more of a specific color system. The first region, which is a specific color that is one of a primary color that corresponds to one component of the components and a secondary color that corresponds to two components. Area identification part of
A correction unit that generates corrected image data by executing correction for reducing a specific component with respect to the values of a plurality of pixels in the specified specific color region, and the specific component is the specific color. The correction unit, which is a component different from the corresponding component,
An image processing device comprising. The image processing apparatus according to claim 1, further
A generation unit that generates print data for executing printing using a plurality of color materials using the corrected image data is provided.
The image processing apparatus, which is a correction for reducing the amount of at least one color material used among the plurality of color materials, is a correction for reducing the specific component. The image processing apparatus according to claim 1 or 2.
The target image data is an image processing device that indicates color for each pixel with a color value of a first color system including components corresponding to a plurality of signals output by the image sensor. The image processing apparatus according to claim 3, further
The value of each pixel of the target image data is converted from the color value of the first color system to the color value of the second color system having a plurality of components corresponding to the plurality of color materials. It is equipped with a color conversion unit that generates converted image data.
The correction unit executes a correction for reducing the specific component with respect to the color value of the second color system of the plurality of pixels in the specific color region in the converted image data, and performs the correction. An image processing device that generates finished image data. The image processing apparatus according to any one of claims 1 to 4.
The first region specifying unit determines whether or not the block is the specific color region by using the values of the plurality of pixels in the block for each of the plurality of blocks set in the target image. An image processing device that identifies the specific color region by making a determination. The image processing apparatus according to any one of claims 1 to 5, further comprising.
Using the target image data, a second area specifying unit that specifies a character area indicating a character in the target image and a non-character area indicating an image different from the character, and
A smoothing processing unit that executes smoothing processing on the non-character area,
With
The first region specifying unit is an image processing apparatus that identifies the specific color region using the smoothed target image data. The image processing apparatus according to any one of claims 1 to 6, further comprising.
Using the target image data, a second area specifying unit that specifies a character area indicating a character in the target image and a non-character area indicating an image different from the character, and
A smoothing processing unit that executes smoothing processing on the non-character area,
With
The correction unit is an image processing apparatus that executes correction for reducing a specific component by using the target image data that has been smoothed. The image processing apparatus according to any one of claims 1 to 6.
The specific color region is a region in which the hue corresponds to any one of the primary color and the secondary color, and is represented by a color whose saturation and lightness are equal to or higher than the reference. apparatus. It ’s a computer program
An image acquisition function that acquires target image data generated using an image sensor,
It is a first region specifying function for specifying a specific color region in a target image based on the target image data by using the target image data, and the specific color region is three or more of a specific color system. The first region, which is a specific color that is one of a primary color that corresponds to one component of the components and a secondary color that corresponds to two components. Area identification function and
A correction unit that generates corrected image data by executing correction for reducing a specific component with respect to the values of a plurality of pixels in the specified specific color region, and the specific component is the specific color. The correction function, which is a component different from the corresponding component,
A computer program that makes a computer realize.

Images