JP2023034014A - Image processing device, control method of the same, and program - Google Patents

Abstract

To improve show-through correction to execute on an image of image data obtained by scanning, in an image processing device 100.SOLUTION: An image processing device 100, which acquires image data obtained by scanning and outputs it, extracts image areas obtained by dividing into a plurality of pieces an image of the image data obtained by scanning out of the image, divides the image by each extracted image area, executes show-through correction concerning the image of the image data obtained by scanning, and outputs show-through-corrected image data.SELECTED DRAWING: Figure 2

Description

The present invention relates to an image processing device, its control method, and a program.

Japanese Patent Application Laid-Open No. 2002-200000 discloses a copier that scans a document, acquires image data from the scan, and outputs the image data. Further, in the copier disclosed in Japanese Patent Application Laid-Open No. 2002-200011, show-through correction processing can be executed for an image of image data obtained by scanning. Show-through refers to a state in which the image data of the front side of the scanned document contains the image component of the back side of the document.

Japanese Patent No. 6182092

However, even if the show-through correction process of Patent Document 1 is used, it is difficult to reproduce the colors in the image data after the show-through correction process when the document is color-coded with a plurality of colors, for example. Sometimes. Also, if the show-through correction process is executed to suppress such color fading, the show-through correction process of Japanese Patent Application Laid-Open No. 2002-200013 may not be able to satisfactorily suppress the show-through component.

As described above, the image processing apparatus is required to improve the show-through correction process.

An image processing apparatus according to the present invention is an image processing apparatus for outputting image data obtained by scanning, and extracting means for extracting an image area obtained by dividing the image into a plurality of parts from the image of the image data obtained by scanning. a correcting means for performing show-through correction processing on the image of the image data obtained by scanning, divided into image regions extracted by the extracting means; and an output for outputting image data corrected for show-through by the correcting means. means.

According to the present invention, the show-through correction process can be improved by correcting the show-through for each image area of the scanned image data.

1 is a block diagram showing the configuration of an image processing device according to a first embodiment of the present invention; FIG. 2 is a flowchart of show-through removal control by the image processing apparatus of FIG. 1; FIG. 4 is an explanatory diagram of an example of a pixel group made up of a pixel of interest and peripheral pixels; 4 is a flow chart of labeling control for each image area in a variance image. FIG. 10 is an explanatory diagram of image region extraction processing and labeling using continuous variance values; 3 is a flowchart of image area extraction control in FIG. 2; FIG. 4 is an explanatory diagram of an example of pixel extraction for each image region to which a label is assigned; 9 is a flow chart of show-through removal control in the image processing apparatus according to the second embodiment of the present invention;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the configurations described in the following embodiments are merely examples, and the scope of the present invention is not limited by the configurations described in the embodiments.

[First embodiment]
FIG. 1 is a block diagram showing the configuration of an image processing apparatus 100 according to the first embodiment of the invention. The image processing apparatus 100 of FIG. 1 has an external storage device 105, a RAM 111, a ROM 113, and a CPU 112 to which these are connected, for example, by an internal bus (not shown). The CPU 112, RAM 111, and ROM 113 may be provided in the image processing apparatus 100 by a one-chip microcomputer. A computer interface 101 to which the computer device 111 is connected, a scanner 102, a user interface (UI) 103, and a print engine 104 may be connected to the CPU 112 .

An image processing apparatus 100 in FIG. 1 is an example of an image forming apparatus called an MFP (MultiFunction Peripheral). Such an image processing apparatus 100 in FIG. 1 can, for example, scan a document with the scanner 102 and acquire image data of the document by scanning. Further, the image processing apparatus 100 may acquire scanned image data from the computer device 111 through the computer interface 101 . Further, the image processing apparatus 100 in FIG. 1 can print the acquired image data on paper or the like using the print engine 104 and output the data to the computer apparatus 111 through the computer interface 101 . The user interface 103 has, for example, a touch panel, numeric keys, and a start button operated by the user using the image processing apparatus 100 . The operation screen can be switched and displayed on the touch panel. The external storage device 105 may be, for example, a device that can be removed from the image processing apparatus 100 and replaced, and may be a semiconductor memory such as an SSD, an HDD, or the like. The RAM 111 is used as a main memory of the CPU 112 and a temporary storage area such as a work area. ROM 113 stores control programs and data. The CPU 112 reads the control program stored in the ROM 113, develops it in the RAM 111, and executes the control program. Thereby, the CPU 112 functions as a control unit that controls the overall operation of the image processing apparatus 100 . 1, the CPU 112 includes an image data acquisition unit 106, a show-through removal unit 107, a color conversion unit 108, a gamma correction unit 109, an image forming unit 110, is realized.

An image data acquisition unit 106 acquires RGB image data generated from a document read by the scanner 102 . The RGB image data has 8-bit data having any value from 0 to 255 as each of R, G, and B color components. RGB image data may also include, for example, 10-bit data for each color component and 16-bit data for each color component. Note that the image data of the document read by the scanner 102 may not be the RGB image data described above. The image data of the document read by the scanner 102 may be image data having a color space that can be converted into a CMYK space, which will be described later. The image data acquisition unit 106 may obtain the image data of the document read by the scanner 102 from the computer device 111 . Also, the image data acquisition unit 106 may acquire various setting values transmitted from the user interface 103 .

The show-through removal unit 107 performs show-through removal processing on the image of the RGB image data acquired by the image data acquisition unit 106 from the scanner 102, and generates RGB image data from which show-through has been removed. Details of the show-through removal control by the show-through removal unit 107 will be described later.

The color conversion unit 108 converts each RGB color value for each pixel value of the RGB image data processed by the show-through removal unit 107 into each CMYK color value to generate CMYK image data. The color conversion unit 108 performs an interpolation calculation using, for example, an LUT (Look Up Table) that associates RGB and CMYK, and converts each RGB color value for each pixel value into each CMYK color value. you can A gamma correction unit 109 performs correction processing on the CMYK image data processed by the color conversion unit 108 to keep the gradation characteristics of the print engine 104 constant. The image forming unit 110 converts the CMYK image data processed by the gamma correction unit 109 into N (integer)-bit halftone image data suitable for a printer, and outputs the halftone image data to the print engine 104 . The image forming unit 110 may convert to halftone image data using various processing methods such as a density pattern method, a systematic dither method, an error diffusion method, etc. as halftone processing. Note that the print engine 104 will be described here as an engine using CMYK toners. However, the print engine 104 may be an engine using CMYK inks.

FIG. 2 is a flow chart of the show-through removal control by the image processing apparatus 100 of FIG. The CPU 112 of the image processing apparatus 100 may act as the show-through removal unit 107 to perform the show-through removal control of FIG. The CPU 112 reads the control program stored in the ROM 113, develops it in the RAM 111, and executes it.

In step S<b>201 , the CPU 112 acquires RGB image data of the document read by the scanner 102 from the image data acquiring unit 106 .

In step S202, the CPU 112 acquires one pixel as a pixel of interest from the RGB image data acquired in step S201. In this embodiment, each pixel position of the RGB image data is represented by a (x, y) coordinate position. x is the pixel position in the horizontal direction of the image, and the value increases toward the right side of the image. y is the pixel position in the vertical direction of the image, and the lower the image, the larger the numerical value. In processing the RGB image data, the CPU 112 first counts up x by 1 from 0, and when x reaches the maximum value (the maximum number of pixels in the horizontal direction of the image), returns x to 0 and y from 0 to 0. You can count up by 1. By repeating this, the CPU 112 can sequentially acquire all the pixels of the RGB image data one by one.

In step S203, the CPU 112 acquires peripheral pixels around the target pixel acquired in step S202. Here, the CPU 112 may acquire, for example, 5×5 pixels (25 pixels including the pixel of interest) centered on the pixel of interest from the RGB image data.

FIG. 3 is an explanatory diagram of an example of a pixel group made up of a pixel of interest and peripheral pixels. FIG. 3 is an image diagram of 5×5 pixels obtained from RGB image data. FIG. 3A is an image 401 based on RGB image data. The CPU 112 basically acquires a pixel of interest 402 and 24 peripheral pixels 403 around the pixel of interest 402 from an image 401 of image data, as shown in FIG. 3B. However, if the pixel of interest 404 is positioned at the edge of the image, as shown in FIG. . In this case, the CPU 112 may supplement the RGB values of peripheral pixels that cannot be acquired with zeros. For example, when the pixel of interest 404 is at the left edge of the image 401 of the image data as shown in FIG. The RGB value of pixel 405 is taken as 0. In the description here, the number of pixels in the pixel group acquired in steps S202 and S203 is 5×5=25 pixels, but the CPU 112 may acquire other number of pixels. The CPU 112 may change the number of pixels acquired in steps S202 and S203 according to the total number of pixels and size of the image data.

In step S204, the CPU 112 calculates the variance of the pixel of interest based on the pixel group of the pixel of interest and its surrounding pixels acquired in steps S202 and S203. The CPU 112 calculates the variance value of the pixel of interest for each of R, G, and B. FIG. The CPU 112 may calculate a variance value for each color component using a group of formulas shown in Formula 1 below. Here, σr[n] is the R (red) variance value of the n-th pixel to be processed in the image data. xri is the i-th R pixel value in the 5×5 pixel group. μr is the average value of R for a group of 5×5 pixels. Similarly, σg[n] is the G (green) variance value of the n-th pixel of the image data to be processed. xgi is the i-th G pixel value in the 5×5 pixel group. μg is the average value of G of a 5×5 pixel group. σb[n] is the B (blue) variance value of the n-th pixel of the image data to be processed. xbi is the i-th B pixel value in the 5×5 pixel group. μb is the average value of B for a group of 5×5 pixels. N is the total number of pixels in the 5×5 pixel group (here, 25). Σ represents the summation calculation. As a result, the CPU 112 can generate the variance value of the center pixel from the value of the center pixel and the values of the peripheral pixels sequentially selected in the image of the scanned image data.

In step S205, the CPU 112 determines whether the processes from steps S202 to S204 have been executed for all pixels of the RGB image data acquired in step S202. The CPU 112 may assign a flag indicating whether or not processing has been completed to each pixel, and may determine that all pixels have been processed when the flags of all pixels indicate that processing has been completed. Alternatively, the CPU 112 counts and holds the total number of pixels of the image data when obtaining the image data, and when the number of times of loop processing from step S202 to step S204 matches the count value, all the pixels are counted. can be considered to have been processed. If all pixels have not been processed, CPU 112 returns the process to step S202. After processing all pixels, CPU 112 advances the process to step S206. As a result, the CPU 112 can generate a variance value image having the same pixel arrangement as that of the scanned image data and having variance values for each image. The CPU 112 may record and hold the variance value image data in the external storage device 105 .

In step S206, CPU 112 extracts one or more image regions from the scanned image data. The CPU 112 extracts continuous image regions having, for example, the same variance value in the variance value image. The CPU 112 extracts an area corresponding to the image area extracted from the variance value image as an image area based on the correspondence relationship between the variance value image and the scanned image data. As a result, the CPU 112 can extract an image area obtained by dividing the image into a plurality of parts from the image of the scanned image data. The CPU 112 can extract a range in which the variance values are continuous as one image region based on the distribution of the variance values in the corresponding variance value image with the same pixel arrangement as the scanned image data. The details of image area extraction control will be described later.

In step S207, the CPU 112 selects one of the image regions extracted in step S207.

In step S208, the CPU 112 extracts the maximum pixel value of the selected image area. As a result, the CPU 112 can extract the maximum pixel value for each image area as a show-through correction amount for correcting show-through for each image area.

In step S209, the CPU 112 calculates a show-through correction value for each pixel in the selected image area. Here, the CPU 112 may correct the value of each pixel included in the extracted image area using the maximum value of the pixels extracted as the show-through correction amount multiplied by a predetermined ratio.

In step S210, the CPU 112 uses the show-through correction value for each pixel in the selected image area to correct the value of each pixel in the selected image area.

In step S211, the CPU 112 determines whether or not the show-through correction processing has been completed for all of the image regions extracted in step S207. If the processing has not been completed for all image areas, the CPU 112 returns the processing to step S207. The CPU 112 repeatedly executes the show-through correction process for each extracted image area from step S207 to step S211 until the process for all image areas is completed. As a result, the CPU 112 can correct the value of each pixel included in each extracted image area using the maximum value of the extracted pixels as the show-through correction amount for each extracted image area. . After completing the processing for all image regions, CPU 112 advances the processing to step S212.

In step S212, the CPU 112 outputs the image data after the show-through correction as scanned image data. The image data that has undergone show-through correction is successively processed by, for example, the color conversion unit 108, the gamma correction unit 109, and the image forming unit 110, and is output to the print engine 104, for example. As a result, the image processing apparatus 100 can print an image on paper or the like based on image data in which show-through is suppressed when scanning a document.

FIG. 4 is a flow chart of labeling control for each image area in a variance image. The CPU 112 of the image processing apparatus 100, as an extracting unit, first executes labeling control for each image area in FIG. 4 in image area extraction control in step S206 in FIG. By the processing up to step S205, the CPU 112 has finished calculating variance values for all pixels of the scanned image data using the pixel group in FIG. The CPU 112 generates a variance image having the same number of pixels as the scanned image data.

In step S<b>301 , the CPU 112 reads and acquires the variance image stored in the external storage device 105 from the external storage device 105 .

In step S302, the CPU 112 converts the variance value image acquired in step S301 into a predetermined number of gradations. The conversion of the number of gradations here changes the value of each pixel (distribution value here) to a representative value without changing the 8-bit data format that can take values from 0 to 255 for each pixel. It means the process to do. Moreover, when the predetermined number of gradations is 8 gradations, the eight representative values may be mutually discrete values. Here, eight representative values, such as 0, 36, 73, 109, 146, 182, 219, and 255, may be used. In this case, the CPU 112 converts the value of each pixel into one value selected from those eight representative values. In this way, the values of the pixels included in the range where the variance values are continuous are similar to the variance values, even if the original values are different. can be converted to the same representative value based on As conversion means, the CPU 112 converts the variance value of each image of the variance value image into discrete gradation numbers.

The CPU 112 preferably converts the variance value of each pixel into a representative value for each range of variance values using the group of equations 2 below. Here, m is the number of gradations. The number m of gradations can be set arbitrarily. If the number of gradations is not converted, m should be set to 255. round(X) is an operation for rounding X to the first decimal place. Dis[i] is the i-th representative value corresponding to the number of gradations to be converted. Spr[n] is the R (red) pixel value (variance value) of the n-th pixel after tone number conversion. σr[n] represents the R variance value of the n-th pixel of the image data. Similarly, Spg[n] is the G (green) pixel value (variance value) of the n-th pixel after tone number conversion. σg[n] is the G variance value of the n-th pixel of the image data. Spb[n] is the B (blue) pixel value (dispersion value) of the n-th pixel after tone number conversion. σb[n] is the B variance value of the n-th pixel of the image data. This group of formulas 2 is for conversion into a plurality of representative values that equally divide the range of values from 0 to 255. The CPU 112 may convert to a plurality of representative values having arbitrary discrete relationships that are not equal.

Dis[i]=round(255/(m-1))×i (i is an integer 0<=i<=m-1)
Spr[n]=Dis[i] (if Dis[i]<=σr[n]<Dis[i+1])
Spr[n]=255 (when σr[n]=255)
Spg[n]=Dis[i] (if Dis[i]<=σg[n]<Dis[i+1])
Spg[n]=255 (if σg[n]=255)
Spb[n]=Dis[i] (if Dis[i]<=σb[n]<Dis[i+1])
Spb[n]=255 (when σb[n]=255) ・・・Formula 2

In step S303, the CPU 112 selects, for example, a 3×3 pixel group (9 pixels) including one pixel of interest and its surrounding pixels from the variance value image that has undergone tone number conversion. As a result, the CPU 112, as a selection unit, selects an arbitrary pixel in the variance value image as the pixel of interest and selects pixels surrounding the pixel of interest. Note that the CPU 112 executes the processing from step S303 to step S305 for each of the R, G, and B color components based on the variance value of each pixel. Also, the pixel group to be selected is not limited to 3.times.3, and may be 5.times.5 or 7.times.7, for example. Here, the CPU 112 may set the pixel group including the upper left pixel of the variance value image, that is, the pixel of (x, y)=(0, 0) as the starting position. Also, the CPU 112 counts up x from 0 from the starting position, and when x reaches the maximum value (maximum number of pixels in the horizontal direction of the image), returns x to 0 and counts up y from 0. , all pixels can be selected in turn by pixel groups. This allows the CPU 112 to sequentially select all pixels of the variance value image.

In step S304, the CPU 112 detects continuity for the pixel group selected from the variance value image subjected to the tone number conversion in step S303. The CPU 112 may determine that there is continuity when the same variance values are continuous in the pixel group selected from the variance value image subjected to gradation number conversion in step S303. Thereby, the CPU 112, as a determination unit, compares the pixel of interest and the peripheral pixels in the variance value image to determine the continuity of the variance values in the variance value image.

In step S305, the CPU 112 assigns a label according to the determination result of step S304. The CPU 112 assigns the same label indicating continuity to a plurality of pixels having continuity in the selected pixel group. Pixels that are not continuous in the selected group of pixels are given different labels by the CPU 112 to indicate that they are not continuous. Thus, for example, if adjacent pixels have continuity, these pixels will be labeled with the same value. Conversely, if adjacent pixels are discontinuous, they will be labeled with different values. The value of the label may be a value that is incremented by 1, ie, 1, 2, 3, . As a result, the CPU 112, as the labeling means, assigns a label as one image area to the pixel range in the variance value image determined by the determining means to have continuity in the variance values.

In step S306, the CPU 112 determines whether or not the above-described processing has been performed for all pixels of the variance value image that has undergone tone number conversion. For example, the CPU 112 counts and holds the total number of pixels of the variance value image subjected to the tone number conversion, and processes all the pixels when the number of times of loop processing from step S303 to step S306 matches the count value. It can be judged that If the above-described processing has not been completed for all the pixels of the variance value image that has undergone gradation number conversion, the CPU 112 returns the processing to step S303. After executing the above-described processing for all the pixels of the variance value image subjected to the tone number conversion, the CPU 112 advances the processing to step S307.

In step S307, the CPU 112 records and retains in the external storage device 105 the last label number assigned to all pixels of the variance value image subjected to the tone number conversion. For example, if the label is incremented from 0 to 5, CPU 112 records and holds the final label number “5” in external storage device 105 .

FIG. 5 is an explanatory diagram of image region extraction processing and labeling using continuous variance values. FIG. 5A shows a 3×3 pixel group. The pixel group in FIG. 5A includes a pixel of interest 501 and surrounding pixels 502 for one week. FIG. 5B shows the variance values subjected to the tone number conversion in the 3×3 pixel group 506 in the initial state immediately after starting the processing of the variance value image subjected to the tone number conversion, and and the label value given to the 3×3 pixel group 508 . A 3×3 pixel group 506 in FIG. 5B includes pixels having a variance value of 36 and pixels having a variance value of 73 after tone number conversion. Immediately after the start of processing, 0 and 1 are given to the corresponding 3×3 pixel group 508 according to the variance value of each pixel. FIG. 5C shows the variance values subjected to the gradation number conversion in the 3×3 pixel group 509 after the processing of the variance value image subjected to the gradation number conversion has progressed, and the values assigned thereto. and the label values of the 3×3 pixel group 511 . A 3×3 pixel group 509 in FIG. 5C includes pixels having a variance value of 36 and pixels having a variance value of 73 after tone number conversion, as in FIG. 5B. After the processing has progressed, the corresponding 3×3 pixel group 511 is given 1 and 2 according to the variance value of each pixel.

Note that here, the CPU 112 assigns only the target pixel a label with a value corresponding to the difference with the variance values of the surrounding pixels. On the other hand, the CPU 112 may label a plurality of pixels with values corresponding to differences with the variance values of surrounding pixels. In this case, the CPU 112 can reduce the number of loops from step S303 to step S306. Also, here, the CPU 112 detects continuity while shifting the pixel of interest pixel by pixel starting from the pixel at the upper left position (x, y)=(0, 0) of the variance image. Alternatively, for example, CPU 112 may start processing from a pixel other than the upper left pixel of the variance image. Further, the CPU 112 may exclude pixels that have already been labeled from the selection of the pixel of interest. When one variance value image subjected to tone number conversion in this way contains a plurality of variance values based on representative values, the final label number must be at least equal to or greater than the number of types of representative values contained in the variance value image. value can be incremented. For example, the upper left region of the variance value image can be labeled 0 value region, the region to the right of it can be labeled 1 value region, and the region to the right of it can be labeled 2 value region. Label values may be similarly incremented in the vertical direction of the variance value image.

FIG. 6 is a flow chart of image area extraction control in FIG. In step S206, the CPU 112 first executes labeling control for each image area in the variance image of FIG. The CPU 112 assigns a label with a common value to a plurality of continuous pixels in the variance value image subjected to the tone number conversion, and assigns a label with a different value to a plurality of non-continuous pixels. be able to. Thereby, the CPU 112 can obtain information for recognizing the image area in the image of the image data in the variance image. After that, the CPU 112 executes the image area extraction control of FIG. 6 in step S206. Through this series of processing, the CPU 112 can extract an image region from the image data based on the image region in the variance value image that has undergone tone number conversion. Then, the CPU 112 can select one image area extracted from the image data in step S207 of FIG. 4, for example, and repeatedly execute the subsequent show-through correction processing for each selected image area. be possible.

In step S801, the CPU 112 acquires a reference label number. The label number acquired here may be one number among a plurality of label values given to the variance value image subjected to the tone number conversion in the processing of FIG. First, the CPU 112 may acquire 0 as the first reference label number, for example.

In step S802, the CPU 112 reads out from the external storage device 105 the variance value image calculated in steps S202 to S205 of FIG. select. In selecting the pixels of the variance value image, as in step S202 in FIG. 2, each pixel position is represented by the coordinate position of (x, y) in the variance value image. y is the vertical axis, and the downward direction is the positive direction. When the pixels of the variance value image are selected in order, the CPU 112 first counts up x from 0, and when x reaches the maximum value (the maximum number of pixels in the horizontal direction of the image), returns x to 0. Count up y from 0. By repeating this, the CPU 112 can sequentially select all the pixels of the variance value image one by one.

In step S803, the CPU 112 acquires the label number assigned to the pixel selected for the variance value image in step S802.

In step S804, the CPU 112 determines whether the reference label number acquired in step S801 and the label number of the selected pixel acquired in step S803 are identical. If these label numbers match equally, CPU 112 advances the process to step S805. If these label numbers are not equal, CPU 112 skips step S805 and advances the process to step S806.

In step S<b>805 , the CPU 112 stores in the external storage device 105 acquired pixels determined to have the same label number as the reference. CPU 112 may, for example, copy the corresponding pixels from the original image data and store them in external storage device 105 . In addition, for example, the CPU 112 may hold information for referring to the corresponding pixel of the image data, such as coordinate information of the corresponding pixel of the image data, in the external storage device 105 .

In step S806, the CPU 112 determines whether or not the selection described above has been completed for all pixels of the variance image. For example, the CPU 112 assigns a flag indicating whether or not each pixel has been selected, and determines that all pixels of the variance image have been selected when the flags of all pixels indicate that they have been selected. good. Further, the CPU 112 may hold the total number of pixels, and when the number of times of selection matches the total number of pixels, it may be determined that the selection of all pixels of the variance value image has been completed. If the selection of all pixels of the variance value image has not been completed, the CPU 112 returns the process to step S802. The CPU 112 repeats the processing from step S802 to step S806 until all pixels of the variance image are selected.

Then, the CPU 112 repeats the image area extraction control in FIG. 6 while incrementing the reference label number obtained in step S801 by one. 6 when the reference label number acquired in step S801 matches the final label number stored in step S307 of FIG. terminates the control of FIG.

As a result, the external storage device 105 retains information for classifying and organizing all the pixels of the variance value image for each label number. A plurality of pixels of image data are classified by label number. Image area information for each label number is generated and held in the external storage device 105 .

FIG. 7 is an explanatory diagram of an example of pixel extraction for each labeled image region. FIG. 7A is a variance value image 701 that has undergone tone number conversion. Label numbers from 1 to 3 are assigned to all pixels of the variance value image 701 in FIG. The CPU 112 may select a reference label number, select pixels in order from, for example, the upper left pixel of the variance value image 701 in FIG. 7A, and execute the image region extraction control in FIG. FIG. 7B shows an image region 702 of pixels with label number 1 selected from the variance value image 701 of FIG. 7A. The CPU 112 sets the reference label number to 1 and repeats the processing from step S801 to step S806 for all pixels. can be generated and stored. An image area 702 is composed of a plurality of pixels labeled with a number of 1. FIG. FIG. 7C is an image area 703 of pixels with label number 2 selected from the variance value image 701 of FIG. 7A. The CPU 112 sets the reference label number to 2 and repeats the processing from step S801 to step S806 for all pixels. can be generated and stored. An image area 703 is composed of a plurality of pixels to which the label number 2 is assigned. FIG. 7(D) is an image area 704 of pixels with label number 3 selected from the variance value image 701 of FIG. 7(A). The CPU 112 sets the reference label number to 3 and repeats the processing from step S801 to step S806 for all pixels. can be generated and stored. An image area 704 is composed of a plurality of pixels labeled with the number 3. FIG.

After generating the image area by the processing in FIG. 6, the CPU 112 executes the processing after step S207 of the show-through removal control in FIG. Execute control for suppression for each region. Based on the above description, the processing from step S207 to step S210, which is repeated for each image area, will be described below.

In step S207, the CPU 112 selects one image area from one or more image areas extracted from the image data.

In step S208, the CPU 112 extracts the maximum pixel value among the pixels in the image area selected in step S207. For example, the CPU 112 selects the pixel value of one pixel from among a plurality of pixels included in the selected image area of the image data, and compares the pixel values of other pixels included in the same image area. compare. The CPU 112 then selects the larger one of these two pixel values and compares it with the pixel values of other pixels. CPU 112 repeats the pixel comparison for all of the plurality of pixels contained in the selected image region of the image data. When the comparison of the pixel values of all the pixels included in the image area is completed, the CPU 112 selects and extracts the maximum pixel value in the image area. Note that the CPU 112 may extract the maximum pixel value in the image area by another method.

In step S209, the CPU 112 calculates a show-through correction value for each pixel in the image area selected for the image data. In this embodiment, the CPU 112 calculates the magnitude (absolute value) of the difference between the maximum pixel value in the region extracted in step S208 and the pixel value of the target pixel being processed as the show-through correction value.

The CPU 112 may calculate the show-through correction value by using the following formula 3. where n represents the nth pixel in the image area. abs(X) means the absolute value operation of X. Dr[n] is the R (red) show-through correction value for the n-th pixel. Zr[n] is the R (red) pixel value of the n-th pixel. MAXr[n] is the maximum pixel value of R in the region extracted in step S208. Zg[n] is the G (green) pixel value of the n-th pixel. MAXg[n] is the maximum pixel value of G in the region extracted in step S208. Zb[n] is the B (blue) pixel value of the n-th pixel. MAXb[n] is the maximum pixel value of B in the region extracted in step S208.

Dr[n]=abs(Zr[n]-MAXr[n])
Dg[n]=abs(Zg[n]-MAXg[n])
Db[n]=abs(Zb[n]-MAXb[n]) ・・・Formula 3

In step S210, the CPU 112 corrects the pixel value of each pixel in the image area using the show-through correction value calculated in step S209. The CPU 112 may calculate the show-through correction value using the following formula 4. Here, M is the smallest value among Dr, Dg, and Db calculated in step S209. Min(X1, X2, X3) means a function that selects the minimum value among X1 to X3. Ep is a show-through removal parameter. The show-through removal parameter is a value for controlling the correction rate of each pixel at the show-through location, and indicates the correction rate for each of the R, G, and B values of the target pixel. For example, when the show-through removal strength is set to weak, 20% of the correction amount is added to the RGB values of the target pixel. The show-through removal parameter in this case is 0.2. By using the show-through removal parameter, it is possible to make adjustments such as reducing the show-through correction amount for dark color pixels in which show-through is unlikely to occur in the first place. Xmr[n] is the corrected R (red) pixel value for the n-th pixel. Xmg[n] is the corrected G (green) pixel value for the n-th pixel. Xmb[n] is the corrected B (blue) pixel value for the n-th pixel.

M=Min(Dr[n], Dg[n], Db[n])
Xmr[n] = Zr[n] + (M x Ep)
Xmg[n] = Zg[n] + (M x Ep)
Xmb[n]=Zb[n]+(M×Ep) Equation 4

The CPU 112 repeats such show-through correction processing for each image area of the scanned image data. As a result, the CPU 112 can satisfactorily remove the show-through image component included in the image of the scanned image data for each image area of the image data rather than uniformly from the entire image of the image data.

As described above, in the present embodiment, an image of image data obtained by scanning is extracted into a plurality of image regions obtained by dividing the image, and the image of image data obtained by scanning is divided into each of the extracted image regions. perform the show-through correction process. In this embodiment, the show-through correction process can be performed on each image area of the image of the image data instead of performing one show-through correction process on the entire image. As a result, in the present embodiment, for example, in an image of image data obtained by scanning a document that is color-coded with a plurality of colors, in calculating the show-through correction amount, a portion that is not related to show-through related to correction is referred to. It is possible to suppress In this embodiment, in an image of image data obtained by scanning a document that is color-coded with a plurality of colors, show-through correction processing is performed so as to suppress show-through components while ensuring color reproducibility for each image area. can be executed.

For example, an image 401 of RGB image data in FIG. 3A includes a rectangular image component 406 due to show-through and a character image component 407 due to show-through. The rectangular image component 406 due to the show-through or the character image component 407 due to the show-through has a density and color close to the dark-colored rectangular image component 408 included in the image of the image data. there is In this case, the image area of the rectangular image component 406 due to show-through or the image area of the character image component 407 due to show-through has the same value as the image area of the rectangular image component 408 . Therefore, when the show-through correction process is uniformly applied to the entire image, not only the image area of the image component 406 with show-through and the image area of the image component 407 with show-through, but also the original image data The image quality of the image component 408 also changes due to the show-through correction processing. On the other hand, in the present embodiment, these image components 406 to 408 are extracted as individual image areas, and show-through correction processing is executed for each image area. In this embodiment, the show-through correction process for the image area of the image component 406 and the show-through correction process for the image area of the image component 407 do not affect the show-through correction process for the image area of the image component 408 . In this embodiment, the image area of the image component 406 with show-through and the image area of the image component 407 are satisfactorily corrected for show-through, and the image quality of the image area of the image component 408 without show-through is changed. You can suppress the show-through correction so as not to cause it.

[Second embodiment]
Next, an image processing apparatus 100 according to a second embodiment of the invention will be described. In the following description, differences from the above-described embodiment will be mainly described.

FIG. 8 is a flowchart of show-through removal control in the image processing apparatus 100 according to the second embodiment of the invention. The CPU 112 of the image processing apparatus 100 may act as the show-through removal unit 107 to perform the show-through removal control of FIG. The processing from step S601 to step S605 and from step S609 to step S614 are the same as the processing from step S201 to step S205 and from step S207 to step S212 in FIG. When CPU 112 determines that all pixels have been processed in step S605, the process proceeds to step S606.

In step S606, the CPU 112, as reduction means, reduces a variance value image having a variance value for each pixel generated based on the scanned image data image. The CPU 112 may thin out the pixels of the variance value image by sampling them to reduce the number of pixels in the variance value image. The CPU 112 may reduce the variance value image using a linear interpolation method based on a preset reduction ratio value, such as 80% or 50%. Instead of using a preset value as the reduction ratio, the CPU 112 may use a value automatically generated for each image data based on the color distribution or variance value distribution of the image data.

In step S607, the CPU 112 extracts one or more image regions from the variance image reduced in step S606. The CPU 112 extracts, for example, a plurality of pixels in a range in which the variance values are continuous in the reduced variance value image, as continuous image regions having the same variance value, by the same extraction control as in the above-described embodiment. you can The CPU 112 converts the variance value obtained for each image of the variance value image into discrete gradation numbers, selects an arbitrary pixel of the variance value image as the pixel of interest, and compares the pixel of interest with the surrounding pixels. By doing so, the continuity of the variance values may be determined. Then, the CPU 112 may assign a label as one image area to the pixel range determined to have continuity in variance values. Since the variance value image to be processed is reduced and the number of pixels to be processed is reduced, the CPU 112 can complete the process in a shorter time than when processing the variance value image before reduction. .

In step S608, the CPU 112 enlarges the variance value image from which the image area is extracted in step S607 so as to have the same size as the scanned image data. The CPU 112 may generate, for example, the value of a pixel with no value for enlargement by linear interpolation processing using the values of a plurality of surrounding pixels with values. CPU 112 may interpolate the label values. After that, the CPU 112 advances the process to step S609, and uses the label value for each pixel of the enlarged variance value image to extract a plurality of pixels corresponding to the label of the same value in the image of the image data from the image area. Select as Also, the CPU 112 corrects the pixel value of each pixel in the selected image area according to the magnitude of the difference between the maximum pixel value in the selected image area and the pixel value of each pixel. Thereby, the CPU 112 can execute the show-through removal process for each image area.

As described above, in the present embodiment, the variance value image obtained by reducing the size of the scanned image data is not converted into a discrete gradation number, and the continuity of the variance value is determined. , labeling for each pixel, and so on. CPU 112 can complete these processes in a short time. Since the processing load can be reduced, the show-through correction processing of this embodiment can be used in various execution environments.

Although the present invention has been described in detail based on its preferred embodiments, the present invention is not limited to these specific embodiments, and various forms without departing from the gist of the present invention can be applied to the present invention. included.

The present invention supplies a program that implements one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and one or more processors of the computer of the system or device reads the program. It is also possible to implement the process executed by The invention can also be implemented by a circuit (eg, an ASIC) that implements one or more functions.

100 image processing device 102 scanner 104 print engine 105 external storage device 107 show-through removal unit 112 CPU
113 ROMs

Claims (9)

An image processing device that acquires and outputs image data obtained by scanning,
extracting means for extracting an image area obtained by dividing the image into a plurality from an image of image data obtained by scanning;
correcting means for performing show-through correction processing on an image of image data obtained by scanning by dividing the image area extracted by the extracting means;
and output means for outputting image data corrected for show-through by the correction means. a variance value generation means for generating a variance value of the center pixel from the value of the center pixel selected from the image of the image data obtained by scanning and the values of the surrounding pixels;
The extraction means is characterized in that, based on a distribution of variance values in a variance value image corresponding to an image of image data obtained by scanning, a range in which variance values are continuous is extracted as one image region. 2. The image processing apparatus according to claim 1. The variance value generating means generates a variance value image having the same pixel arrangement as that of the scanned image data and having variance values for each image,
The extraction means is
conversion means for converting the variance value obtained for each image of the variance value image into a discrete number of gradations;
selection means for selecting an arbitrary pixel in the variance value image as a pixel of interest and selecting pixels surrounding the pixel of interest;
determining means for determining continuity of variance values in the variance value image by comparing the pixel of interest and the peripheral pixels in the variance value image;
3. Labeling means for assigning a label as one image region to a range of pixels in said variance value image determined by said determination means to have continuity of variance values. image processing apparatus as described. reducing means for reducing the number of pixels of the variance value image,
the converting means, the selecting means, and the determining means of the extracting means perform respective processes on the reduced variance value image;
The label means assigns one image to a pixel range corresponding to a range having continuity of variance values determined by the determining means for the reduced variance value image in the variance value image before reduction. 4. The image processing apparatus according to claim 3, wherein a label is given as an area. correction amount acquisition means for acquiring a show-through correction amount for correcting show-through in each extracted image area;
5. The correction unit according to claim 1, wherein, for each extracted image area, the correction means corrects the value of each pixel included in the extracted image area using the show-through correction amount. 1. The image processing apparatus according to item 1. The correction amount acquisition means extracts a maximum value of pixels in each image area as a show-through correction amount for correcting show-through in each image area,
6. An image according to claim 5, wherein said correcting means corrects the value of each pixel included in the extracted image area using the maximum value of said pixels extracted as said show-through correction amount. processing equipment. The correcting means corrects the value of each pixel included in the extracted image area using the maximum value of the pixels extracted as the show-through correction amount multiplied by a predetermined ratio. 7. The image processing apparatus according to claim 6. A control method for an image processing device that acquires and outputs image data obtained by scanning,
A process of extracting an image area obtained by dividing the image into a plurality of parts from an image of image data obtained by scanning;
a process of dividing each extracted image area and executing a show-through correction process on the image of the image data obtained by scanning;
A control method for an image processing apparatus, comprising a process of outputting image data for which show-through has been corrected. A program for causing a computer to execute a control method for an image processing device that acquires and outputs image data obtained by scanning,
The method for controlling the image processing device includes:
A process of extracting an image area obtained by dividing the image into a plurality of parts from an image of image data obtained by scanning;
a process of dividing each extracted image area and executing a show-through correction process on the image of the image data obtained by scanning;
and outputting image data for which show-through has been corrected.

Images