Image processing method and image processing device

Abstract

To determine a cause of an image defect occurring in an image forming apparatus of a tandem system with a smaller number of test images.SOLUTION: A processor 80 specifies a singular part Ps1 composed of a plurality of significant pixels in a color mixture test image g1. In addition, the processor 80 specifies a color vector representing a vector in a color space from one to the other of the color of the singular part Ps1 and the color of a reference area including the periphery of the singular part Ps1 in the color mixture test image g1. Further, the processor 80 determines, based on the color vector specified from one color mixture test image, which of a plurality of imaging units 4x corresponding to a plurality of development colors in an image forming apparatus 2 the cause of an image defect is.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an image processing method and an image processing apparatus for determining the cause of an image defect based on a test image. [Background technology]

[0002] An image forming apparatus such as a printer or a multifunction peripheral performs a print process to form an image on a sheet. During the print process, image defects such as vertical streaks, horizontal streaks, noise spots, and density unevenness may occur in the image formed on the output sheet.

[0003] For example, when the image forming apparatus is an apparatus that executes the printing process by an electrophotographic method, the cause of the image defect is considered to be various parts such as a photoconductor, a charging unit, a developing unit, a transfer unit, etc. Furthermore, skill is required to determine the cause of the image defect.

[0004] It is also known that in an image processing device, a phenomenon that causes vertical streaks, which is an example of an image defect, and characteristic information such as the color, density or screen line number of the vertical streaks are previously associated as table data, and the phenomenon that causes the vertical streaks is identified based on the information on the color, density or screen line number of the image of the vertical streaks in a test image and the table data (see, for example, Patent Document 1).

[0005] The table data is data in which ranges of parameters such as image color, density, screen ruling, etc. are set by threshold values ​​for each type of phenomenon that causes the vertical stripes. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2017-83544 A Summary of the Invention [Problem to be solved by the invention]

[0007] Incidentally, when the image forming apparatus is a tandem type capable of color printing, it is provided with a plurality of image forming units each having a different developing color. Each of the image forming units includes the photoconductor, the charging unit, the developing unit, etc. In general, the developing colors are four colors: cyan, magenta, yellow, and black.

[0008] In the image forming apparatus, it is necessary to determine which of the plurality of image forming units is causing the image defect. In this case, it is considered that the image processing apparatus determines the image defect based on the monochromatic test image for each of the developed colors.

[0009] However, it is preferable that the number of prints of the test image required for determining the image defect is as small as possible in order to reduce the consumption of sheets and developer.

[0010] SUMMARY OF THE PRESENT EMBODIMENT An object of the present invention is to provide an image processing method and an image processing apparatus that are capable of determining the cause of image defects occurring in a tandem type image forming apparatus using a smaller number of test images. [Means for solving the problem]

[0011] An image processing method according to one aspect of the present invention is a method in which a processor determines a cause of an image defect based on a color mixing test image in which a plurality of single-color halftone images corresponding to a plurality of developed colors obtained through an image reading process for an output sheet of an image forming apparatus are combined. The image processing method includes the processor identifying a singular part consisting of a plurality of significant pixels in the color mixing test image. The image processing method further includes the processor identifying a color vector representing a vector in a color space from one of the colors of the singular part and the color of a reference area including a periphery of the singular part in the color mixing test image to the other. The image processing method further includes the processor identifying which of a plurality of imaging units corresponding to the plurality of developed colors in the image forming apparatus is the cause of the image defect based on the color vector identified from one of the color mixing test images.

[0012] An image processing device according to another aspect of the present invention includes a processor that executes the process of the image processing method. Effect of the Invention

[0013] According to the present invention, it is possible to provide an image processing method and an image processing apparatus that are capable of determining the cause of image defects occurring in a tandem image forming apparatus using a smaller number of test images. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing the configuration of an image processing apparatus according to an embodiment. [Diagram 2] FIG. 2 is a block diagram showing the configuration of a data processing unit in the image processing device according to the embodiment. [Diagram 3] FIG. 3 is a flowchart showing an example of a procedure of an image defect determination process in the image processing device according to the embodiment. [Figure 4] FIG. 4 is a flowchart illustrating an example of a procedure of the specific defect determination process in the image processing device according to the embodiment. [Diagram 5]FIG. 5 is a flowchart showing an example of a procedure for density unevenness determination processing in the image processing apparatus according to the embodiment. [Figure 6] FIG. 6 is a diagram showing an example of a test image including a singular portion, and an example of a preprocessed image and a feature image generated based on the test image. [Figure 7] FIG. 7 is a diagram showing an example of a region of interest and adjacent regions sequentially selected from a test image in the main filtering process by the image processing device according to the embodiment. [Figure 8] FIG. 8 is a diagram showing an example of a test image including periodic density unevenness and vertical waveform data derived based on the test image. [Figure 9] FIG. 9 is a flowchart showing an example of a procedure of a feature image generation process in the first application example of the image processing device according to the embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of a procedure of a feature image generation process in the second application example of the image processing device according to the embodiment. [Figure 11] FIG. 11 is a flowchart showing an example of a procedure of feature image generation processing in the third application example of the image processing device according to the embodiment. [Figure 12] FIG. 12 is a flowchart showing an example of a procedure of a feature image generation process in the fourth application example of the image processing device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the following embodiment is an example of the present invention, and is not intended to limit the technical scope of the present invention.

[0016] [Configuration of image processing device 10] The image processing apparatus 10 according to the embodiment includes an image forming apparatus 2 that executes a print process. The print process is a process for forming an image on a sheet. The sheet is an image forming medium such as paper or a sheet-like resin member.

[0017] The image processing device 10 further includes an image reading device 1 that executes a reading process for reading an image from a document. For example, the image processing device 10 is a copying machine, a facsimile machine, a multifunction machine, or the like.

[0018] The image to be printed is an image read from the document by the image reading device 1 or an image represented by print data received from a host device (not shown), etc. The host device is an information processing device such as a personal computer or a mobile information terminal.

[0019] Furthermore, the image forming apparatus 2 may form a predetermined original test image g01 on the sheet by the print process (see FIG. 6). The original test image g01 is an image that is the basis of a test image g1 that is used to determine the presence or absence of an image defect and its cause in the image forming apparatus 2 (see FIG. 6). The test image g1 will be described later.

[0020] The process including the reading process by the image reading device 1 and the print process by the image forming device 2 based on the image obtained by the reading process is a copy process.

[0021] 1, the image forming apparatus 2 includes a sheet transport mechanism 3 and a print unit 4. The sheet transport mechanism 3 includes a sheet sending mechanism 31 and a plurality of pairs of sheet transport rollers 32.

[0022] The sheet sending mechanism 31 sends out the sheet from the sheet storage unit 21 to the sheet transport path 30. A plurality of sheet transport roller pairs 32 transport the sheet along the sheet transport path 30 and discharge the sheet on which an image has been formed onto the discharge tray 22.

[0023] The printing unit 4 performs the printing process on the sheet transported by the sheet transport mechanism 3. In this embodiment, the printing unit 4 performs the printing process by electrophotography.

[0024] The print section 4 includes an image forming section 4x, a laser scanning unit 4y, a transfer device 44, and a fixing device 46. The image forming section 4x includes a drum-shaped photoconductor 41, a charging device 42, a developing device 43, and a drum cleaning device 45.

[0025] The photoconductor 41 rotates, and the charging device 42 uniformly charges the surface of the photoconductor 41. The charging device 42 includes a charging roller 42a that rotates in contact with the surface of the photoconductor 41. The laser scanning unit 4y writes an electrostatic latent image on the charged surface of the photoconductor 41 by scanning with a laser beam.

[0026] The developing device 43 develops the electrostatic latent image into a toner image. The developing device 43 includes a developing roller 43a that supplies toner to the photoconductor 41. The transfer device 44 transfers the toner image on the surface of the photoconductor 41 to the sheet. The toner is an example of a granular developer.

[0027] The fixing device 46 heats the toner image on the sheet to fix it to the sheet. The fixing device 46 includes a fixing rotor 46a that rotates in contact with the sheet, and a fixing heater 46b that heats the fixing rotor 46a.

[0028] 1 is a tandem color printing device capable of executing the printing process of a color image, and therefore the printing unit 4 includes four image forming units 4x each corresponding to a different color toner.

[0029] In the tandem image forming apparatus 2, the transfer device 44 includes four primary transfer rollers 441 corresponding to the four photoconductors 41, an intermediate transfer belt 440, a secondary transfer roller 442, and a belt cleaning device 443.

[0030] The four image forming units 4x respectively form the cyan, magenta, yellow and black toner images on the surface of the photoconductor 41. Each of the primary transfer rollers 441 is also a part of each of the image forming units 4x.

[0031] In each of the image forming units 4x, the primary transfer roller 441 rotates and presses the intermediate transfer belt 440 against the surface of the photoconductor 41. The primary transfer roller 441 transfers the toner image from the photoconductor 41 to the intermediate transfer belt 440. As a result, a color image made up of the four color toner images is formed on the intermediate transfer belt 440.

[0032] In each of the image forming units 4 x , the drum cleaning device 45 removes and collects from the photoconductor 41 the toner that has not been transferred to the intermediate transfer belt 440 and remains on the photoconductor 41 .

[0033] The secondary transfer roller 442 transfers the four-color toner image on the intermediate transfer belt 440 onto the sheet. In the image processing device 10, the photoconductor 41 and the intermediate transfer belt 440 of the transfer device 44 are each an example of an image carrier that carries the toner image and rotates.

[0034] The belt cleaning device 443 removes and collects from the intermediate transfer belt 440 the toner that has not been transferred to the sheet and remains on the intermediate transfer belt 440 .

[0035] 1, the image processing apparatus 10 includes, in addition to the image forming apparatus 2 and the image reading apparatus 1, a data processing unit 8 and a human interface device 800. The human interface device 800 includes an operation unit 801 and a display unit 802.

[0036] The data processing unit 8 executes various data processing operations related to the printing process or the reading process, and further controls various electrical devices.

[0037] The operation unit 801 is a device that accepts operations by a user. For example, the operation unit 801 includes one or both of a push button and a touch panel. The display unit 802 includes a display panel that displays information to be provided to the user.

[0038] As shown in FIG. 2, the data processing unit 8 includes a central processing unit (CPU) 80, a random access memory (RAM) 81, a secondary storage device 82, and a communication device 83.

[0039] The CPU 80 is capable of processing data received by the communication device 83, various types of image processing, and controlling the image forming device 2. The received data may include the print data. The CPU 80 is an example of a processor that executes data processing including the image processing. The CPU 80 may be realized by another type of processor, such as a DSP (Digital Signal Processor).

[0040] The communication device 83 is a communication interface device that communicates with other devices such as the host device through a network such as a LAN (Local Area Network). The CPU 80 performs all data transmission and reception between the CPU 80 and the external devices through the communication device 83.

[0041] The secondary storage device 82 is a computer-readable non-volatile storage device. The secondary storage device 82 stores the computer programs executed by the CPU 80 and various data referenced by the CPU 80. For example, one or both of a flash memory and a hard disk drive are adopted as the secondary storage device 82.

[0042] The RAM 81 is a computer-readable volatile storage device that temporarily stores the computer programs executed by the CPU 80 and data output and referenced in the process of the CPU 80 executing the programs.

[0043] The CPU 80 includes a plurality of processing modules that are realized by executing a computer program. The plurality of processing modules include a main control unit 8a and a job control unit 8b. Note that some or all of the plurality of processing modules may be realized by a processor independent of the CPU 80, such as a DSP.

[0044] The main control unit 8a executes a process of selecting a job in response to an operation on the operation unit 801, a process of displaying information on the display unit 802, a process of setting various data, etc. Furthermore, the main control unit 8a also executes a process of determining the contents of data received by the communication device 83.

[0045] The job control unit 8b controls the image reading device 1 and the image forming device 2. For example, when the received data of the communication device 83 includes the print data, the job control unit 8b causes the image forming device 2 to execute the received print process.

[0046] In addition, when the main control unit 8a detects a copy request operation on the operation unit 801, the job control unit 8b causes the image reading device 1 to execute the reading process, and causes the image forming device 2 to execute the printing process based on the image obtained by the reading process.

[0047] In the printing process, image defects such as vertical streaks Ps11, horizontal streaks Ps12, noise spots Ps13, or density unevenness may occur in the image formed on the output sheet (see FIGS. 6 and 7).

[0048] As described above, the image forming apparatus 2 executes the print process by electrophotography. In this case, the cause of the image defect is considered to be various parts such as the photoconductor 41, the charging device 42, the developing device 43, and the transfer device 44. Skill is required to determine the cause of the image defect.

[0049] In this embodiment, the image forming apparatus 2 executes a test print process for forming a predetermined original test image g01 on the sheet (see FIG. 6).

[0050] For example, when the main control unit 8a detects a test output operation on the operation unit 801, the job control unit 8b causes the image forming device 2 to execute the test print process. In the following description, the sheet on which the original test image g01 is formed is referred to as a test output sheet 9 (see FIG. 1).

[0051] Furthermore, when the test print process is executed, the main control unit 8a causes the display unit 802 to display a predetermined guidance message. This guidance message is a message that prompts the user to set the test output sheet 9 in the image reading device 1 and then to perform a reading start operation on the operation unit 801.

[0052] Then, when the main control unit 8a detects a reading start operation on the operation unit 801 after the guidance message is displayed on the display unit 802, the job control unit 8b causes the image reading device 1 to execute the reading process. As a result, the original test image g01 is read by the image reading device 1 from the test output sheet 9 output by the image forming device 2, and a read image corresponding to the original test image g01 is obtained.

[0053] Then, as described later, the CPU 80 executes a process for determining the presence or absence of the image defect and its cause based on a test image g1 which is the read image or an image obtained by compressing the read image (see FIG. 6). The CPU 80 is an example of a processor that executes the process of an image processing method for determining the presence or absence of the image defect and its cause.

[0054] The device that reads the test image g1 from the test output sheet 9 may be a device other than the image reading device 1, such as a digital camera.

[0055] Incidentally, the image forming apparatus 2 is of a tandem type capable of color printing, and includes a plurality of image forming units 4x each of which develops a different color (see FIG. 1).

[0056] In the image forming apparatus 2, it is necessary to determine which of the multiple imaging units 4x is causing the image defect. In this case, it is considered that the CPU 80 determines the image defect based on a monochrome test image for each of the developed colors.

[0057] However, it is preferable that the number of prints of the test image required for determining the image defect is as small as possible in order to reduce the consumption of sheets and developer.

[0058] The CPU 80 of the image processing device 10 executes an image defect determination process described later (see FIG. 3). This enables the CPU 80 to determine the cause of the image defect occurring in the tandem image forming device 2 with a smaller number of test images g1.

[0059] In addition, the test image g1 may include a plurality of types of image defects. In this case, it is preferable to determine the cause of the image defects by extracting the image defects by type from the images, in order to simplify the determination process and improve the determination accuracy.

[0060] On the other hand, it is preferable that the image processing device 10 is capable of extracting the image defect parts by type from the test image g1 by simple processing and generating an image to be judged.

[0061] By executing the image defect determination process, the CPU 80 can extract the image defect portions by type from the test image g1 through simple processing, and determine the cause of each type of image defect.

[0062] Furthermore, when the cause of the image defect is determined by comparing the value of a specific image parameter, such as the color, density or screen ruling of the image, with a predetermined threshold value, there is a high possibility of overlooking the cause or of making an erroneous determination.

[0063] On the other hand, image pattern recognition processing is suitable for classifying an input image into many events with high accuracy. For example, the pattern recognition processing is processing for determining which of a plurality of event candidates an input image corresponds to by using a learning model trained using sample images corresponding to the plurality of event candidates as training data.

[0064] However, the amount of information in an image is large, and there are multiple types of image defects, such as vertical streaks Ps11, horizontal streaks Ps12, noise points Ps13, and density unevenness (see FIG. 6). Furthermore, there are many possible causes for each type of image defect.

[0065] Therefore, when the test image g1 including the image defect is used as an input image for the pattern recognition process, the amount of calculation required for the pattern recognition process becomes very large, making it difficult to execute the pattern recognition process using a processor included in a multifunction peripheral or the like.

[0066] Furthermore, in order to improve the accuracy of determining the cause of the image defect, a huge amount of the teacher data is required for learning the learning model. However, it takes a lot of time and effort to prepare a huge number of test images g1 corresponding to possible combinations of the types of the image defect and the causes of the image defect for each model of the image forming device 2.

[0067] As described later, in the image defect determination process, the CPU 80 performs image pattern recognition processing in a manner that can reduce the amount of calculation, thereby making it possible to determine the cause of the image defect with high accuracy.

[0068] In the following description, images such as the test image g1 to be processed by the CPU 80 are digital image data. The digital image data constitutes map data including a plurality of pixel values ​​corresponding to two-dimensional coordinate regions in a main scanning direction D1 and a sub-scanning direction D2 intersecting the main scanning direction D1 for each of the three primary colors. The three primary colors are, for example, red, green, and blue. The sub-scanning direction D2 is perpendicular to the main scanning direction D1. The main scanning direction D1 is the horizontal direction in the test image g1, and the sub-scanning direction D2 is the vertical direction in the test image g1.

[0069] For example, the original test image g01 and the test image g1 are mixed-color halftone images formed by combining a plurality of uniform monochromatic halftone images corresponding to a plurality of developed colors in the image forming apparatus 2. The monochromatic halftone images are each uniformly formed at a predetermined intermediate gradation reference density.

[0070] In the test print process, one test output sheet 9 including one original test image g01 is output. Therefore, one test image g1 corresponding to the original test image g01 is the target for identifying the image defect.

[0071] In this embodiment, the test image g1 is a mixed-color halftone image obtained by combining four uniform single-color halftone images corresponding to all the developed colors in the image forming apparatus 2. The test image g1 in this embodiment is an example of a mixed-color test image.

[0072] In addition, the multiple processing modules in CPU 80 further include a feature image generation unit 8c, a singular part identification unit 8d, a color vector identification unit 8e, a periodicity determination unit 8f, a pattern recognition unit 8g and a random unevenness determination unit 8h in order to execute the image defect judgment process (see Figure 2).

[0073] [Image defect judgment processing] An example of the procedure of the image defect determination process will be described below with reference to the flowchart shown in Fig. 3. In the following description, S101, S102, ... represent identification codes of a plurality of steps in the image defect determination process.

[0074] After the guidance message is displayed on the display unit 802, when the reading process is executed in response to the reading start operation on the operation unit 801, the main control unit 8a causes the characteristic image generation unit 8c to execute the processing of step S101 in the image defect judgment processing.

[0075] <Process S101> In step S101, the characteristic image generating unit 8c generates a test image g1 from the read image obtained in the image reading process for the test output sheet 9.

[0076] For example, the characteristic image generating unit 8c extracts a portion of the original image excluding the outer margin area from the read image as a test image g1.

[0077] Alternatively, the characteristic image generating unit 8c generates a test image g1 by a compression process that compresses the portion of the original image excluding the outer margin area from the scanned image to a predetermined standard resolution. The characteristic image generating unit 8c compresses the scanned image when the resolution of the scanned image is higher than the standard resolution. After generating the test image g1, the main control unit 8a shifts the process to step S102.

[0078] <Process S102> In step S102, the feature image generating unit 8c starts a peculiar defect determination process, which will be described later. The peculiar defect determination process is a process for determining the presence or absence of a peculiar part Ps1, such as a vertical streak Ps11, a horizontal streak Ps12, or a noise point Ps13, in the test image g1, and the cause of the occurrence of the peculiar part Ps1 (see FIG. 6). The peculiar part Ps1 is an example of the image defect. Moreover, the test image g1 in this embodiment is an example of a target test image that is a target for identifying the peculiar part Ps1.

[0079] Furthermore, when the specific defect determination process is completed, the main control unit 8a shifts the process to step S103.

[0080] <Process S103> In step S103, the periodicity determination unit 8f starts a density unevenness determination process, which will be described later. Furthermore, when the density unevenness determination process ends, the main control unit 8a shifts the process to step S104.

[0081] <Process S104> In step S104, if the main control unit 8a determines that the image defect has occurred by the process of step S102 or step S103, it shifts the process to step S105, otherwise it shifts the process to step S106.

[0082] <Process S105> In step S105, the main control unit 8a executes a defect handling process that is associated in advance with the type and cause of the image defect that has been determined to have occurred in the process of step S102 or step S103.

[0083] For example, the defect response process includes one or both of a first response process and a second response process shown below. The first response process is a process of displaying a message on the display unit 802 to prompt replacement of a part causing the image defect. The second response process is a process of correcting an image-making parameter to eliminate or mitigate the image defect. The image-making parameter is a parameter related to the control of the image forming unit 4x.

[0084] After executing the defect handling process, the main control unit 8a ends the image defect determination process.

[0085] <Process S106> On the other hand, in step S106, the main control unit 8a issues a normal notification indicating that the image defect has not been identified, and then ends the image defect determination process.

[0086] [Specific defect judgment processing] Next, an example of the procedure of the specific defect determination process in step S102 will be described with reference to the flowchart shown in Fig. 4. In the following description, S201, S202, ... represent identification codes of multiple steps in the specific defect determination process. The specific defect determination process starts from step S201.

[0087] <Process S201> First, in step S201, the feature image generating unit 8c generates a plurality of feature images g21, g22, and g23 by executing a predetermined feature extraction process on the test image g1. Each of the feature images g21, g22, and g23 is an image in which a specific type of singular part Ps1 of the test image g1 is extracted.

[0088] In this embodiment, the multiple characteristic images g21, g22, and g23 include a first characteristic image g21, a second characteristic image g22, and a third characteristic image g23 (see FIG. 6).

[0089] The first characteristic image g21 is an image in which the vertical streaks Ps11 in the test image g1 are extracted. The second characteristic image g22 is an image in which the horizontal streaks Ps12 in the test image g1 are extracted. The third characteristic image g23 is an image in which the noise points Ps13 in the test image g1 are extracted.

[0090] In this embodiment, the feature extraction process includes a first pre-processing process, a second pre-processing process, and a singular part extraction process. In the following description, a pixel sequentially selected from the test image g1 is referred to as a pixel of interest Px1 (see FIGS. 6 and 7).

[0091] The feature image generating unit 8c generates a first preprocessed image g11 by executing the first preprocessing on the test image g1 with the main scanning direction D1 as the processing direction Dx1 (see FIG. 6).

[0092] Furthermore, the feature image generating unit 8c generates a second preprocessed image g12 by executing the second preprocessing on the test image g1 with the sub-scanning direction D2 as the processing direction Dx1 (see FIG. 6).

[0093] Furthermore, the feature image generating unit 8c generates three feature images g21, g22, and g23 by executing the singular part extraction process on the first preprocessed image g11 and the second preprocessed image g12.

[0094] The first pre-processing includes a main filtering process in which the main scanning direction D1 is set as a processing direction Dx1. The main filtering process converts the pixel values ​​of the target pixels Px1 selected sequentially from the test image g1 into conversion values ​​obtained by processing that emphasizes the difference between the pixel values ​​of the target area Ax1 and the pixel values ​​of two adjacent areas Ax2 (see FIGS. 6 and 7).

[0095] The region of interest Ax1 is a region that includes the pixel of interest Px1, and the two adjacent regions Ax2 are regions that are adjacent to both sides of the region of interest Ax1 in a processing direction Dx1 that is set in advance. Each of the region of interest Ax1 and the adjacent region Ax2 is a region that includes one or more pixels.

[0096] The sizes of the attention area Ax1 and the adjacent area Ax2 are set according to the width of the vertical streaks Ps11 or horizontal streaks Ps12 to be extracted, or the size of the noise points Ps13 to be extracted.

[0097] Each of the region of interest Ax1 and the adjacent region Ax2 occupies the same range in a direction intersecting the processing direction Dx1. In the example shown in FIG. 7, the region of interest Ax1 is a region of 21 pixels across 3 columns and 7 rows with the pixel of interest Px1 at the center. Each of the adjacent regions Ax2 is also a region of 21 pixels across 3 columns and 7 rows. Note that in each of the region of interest Ax1 and the adjacent region Ax2, the number of rows is the number of lines along the processing direction Dx1, and the number of columns is the number of lines along the direction intersecting the processing direction Dx1. The size of each of the region of interest Ax1 and the adjacent region Ax2 is set in advance.

[0098] In the main filter process, each pixel value of the region of interest Ax1 is converted to a first correction value using a predetermined first correction coefficient K1, and each pixel value of each adjacent region Ax2 is converted to a second correction value using a predetermined second correction coefficient K2.

[0099] For example, the first correction coefficient K1 is a coefficient of 1 or more by which each pixel value in the region of interest Ax1 is multiplied, and the second correction coefficient K2 is a coefficient of less than 0 by which each pixel value in the adjacent region Ax2 is multiplied. In this case, the first correction coefficient K1 and the second correction coefficient K2 are set so that the sum of the value obtained by multiplying the first correction coefficient K1 by the number of pixels in the region of interest Ax1 and the value obtained by multiplying the second correction coefficient K2 by the number of pixels in the two adjacent regions Ax2 is zero.

[0100] The feature image generating unit 8c derives the first correction value corresponding to each pixel of the attention area Ax1 by multiplying each pixel value of the attention area Ax1 by a first correction coefficient K1, and derives the second correction value corresponding to each pixel of the two adjacent areas Ax2 by multiplying each pixel value of the two adjacent areas Ax2 by a second correction coefficient K2. Then, the feature image generating unit 8c derives a value obtained by integrating the first correction value and the second correction value as the conversion value of the pixel value of the attention pixel Px1.

[0101] Furthermore, the characteristic image generation unit 8c derives the conversion value by adding the sum or average value of multiple first correction values ​​corresponding to multiple pixels in the attention area Ax1 and the sum or average value of multiple second correction values ​​corresponding to multiple pixels in two adjacent areas Ax2.

[0102] For example, it is conceivable that the feature image generating unit 8c generates, as the first preprocessed image g11, first main map data including a plurality of the converted values ​​obtained by the main filtering process in which the main scanning direction D1 is set as the processing direction Dx1.

[0103] As shown in Figure 6, when test image g1 includes one or both of vertical streaks Ps11 and noise points Ps13, the first main map data in which one or both of the vertical streaks Ps11 and noise points Ps13 included in test image g1 are extracted is generated by the main filter processing in which the main scanning direction D1 is the processing direction Dx1.

[0104] Furthermore, when the test image g1 includes horizontal streaks Ps12, the first main map data from which the horizontal streaks Ps12 included in the test image g1 have been removed is generated by the main filtering process in which the main scanning direction D1 is set as the processing direction Dx1.

[0105] The vertical streak Ps11 corresponds to the first unique portion, the horizontal streak Ps12 corresponds to the second unique portion, and the noise point Ps13 corresponds to the third unique portion.

[0106] On the other hand, the second pre-processing includes the main filtering process in which the sub-scanning direction D2 is set as the processing direction Dx1.

[0107] For example, it is conceivable that the feature image generating unit 8c generates, as the second pre-processed image g12, second main map data including a plurality of the integrated values ​​obtained by the main filtering process with the sub-scanning direction D2 as the processing direction Dx1.

[0108] As shown in Figure 6, when test image g1 contains one or both of horizontal streaks Ps12 and noise points Ps13, the second main map data in which one or both of the horizontal streaks Ps12 and noise points Ps13 contained in test image g1 are extracted is generated by the main filter processing in which the sub-scanning direction D2 is the processing direction Dx1.

[0109] Furthermore, when the test image g1 includes vertical streaks Ps11, the second main map data from which the vertical streaks Ps11 included in the test image g1 have been removed is generated by the main filtering process in which the sub-scanning direction D2 is set as the processing direction Dx1.

[0110] However, in the main filter process, an erroneous integrated value having a positive and negative sign opposite to the integrated value expressing the original state of the singular part Ps1 may be derived at the edge parts at both ends of the singular part Ps1 in the processing direction Dx1. If such an erroneous integrated value is processed as a pixel value expressing the singular part Ps1, it may adversely affect the determination of the image defect.

[0111] Therefore, in this embodiment, the first pre-processing further includes an edge enhancement filtering process in which the main scanning direction D1 is the processing direction Dx1, in addition to the main filtering process in which the main scanning direction D1 is the processing direction Dx1.

[0112] Similarly, the second pre-processing further includes the edge emphasis filtering process in which the sub-scanning direction D2 is the processing direction Dx1, in addition to the main filtering process in which the sub-scanning direction D2 is the processing direction Dx1.

[0113] The edge enhancement filter process is a process for performing edge enhancement on the region of interest Ax1 and one of the two adjacent regions Ax2 that is determined in advance.

[0114] Specifically, the edge enhancement filter process converts the pixel value of a pixel of interest Px1, which is sequentially selected from the test image g1, into an edge intensity that combines a third correction value obtained by correcting the pixel value of the region of interest Ax1 with a positive or negative third correction coefficient K3, and a fourth correction value obtained by correcting the pixel value of one of the adjacent regions Ax2 with a fourth correction coefficient K4 whose positive and negative sign is opposite to that of the third correction coefficient K3 (see Figure 6).

[0115] 6, the third correction coefficient K3 is a positive coefficient, and the fourth correction coefficient K4 is a negative coefficient. The third correction coefficient K3 and the fourth correction coefficient K4 are set so that the sum of a value obtained by multiplying the third correction coefficient K3 by the number of pixels in the region of interest Ax1 and a value obtained by multiplying the fourth correction coefficient K4 by the number of pixels in one of the adjacent regions Ax2 is zero.

[0116] The edge emphasis filter process is performed with the main scanning direction D1 as the processing direction Dx1, thereby generating horizontal edge strength map data in which each pixel value of the test image g1 is converted into the edge strength.

[0117] Similarly, the edge emphasis filter process is performed with the sub-scanning direction D2 as the processing direction Dx1, whereby vertical edge strength map data is generated in which each pixel value of the test image g1 is converted into the edge strength.

[0118] In this embodiment, the feature image generating unit 8c generates the first main map data by the main filtering process with the main scanning direction D1 as the processing direction Dx1.

[0119] Furthermore, the characteristic image generating unit 8c generates the horizontal edge strength map data by executing the edge emphasis filter process with the main scanning direction D1 as the processing direction Dx1.

[0120] Furthermore, the characteristic image generating unit 8c generates a first preprocessed image g11 by correcting each pixel value of the first main map data with each corresponding pixel value of the horizontal edge strength map data. For example, the characteristic image generating unit 8c generates the first preprocessed image g11 by adding the absolute value of each pixel value of the horizontal edge strength map data to each pixel value of the first main map data.

[0121] Similarly, the characteristic image generating unit 8c generates the second main map data by executing the main filtering process with the sub-scanning direction D2 as the processing direction Dx1.

[0122] Furthermore, the characteristic image generating unit 8c generates the vertical edge strength map data by executing the edge emphasis filter process with the sub-scanning direction D2 as the processing direction Dx1.

[0123] Furthermore, the feature image generating unit 8c generates a second preprocessed image g12 by correcting each pixel value of the second main map data with each corresponding pixel value of the vertical edge strength map data. For example, the feature image generating unit 8c generates the second preprocessed image g12 by adding the absolute value of each pixel value of the vertical edge strength map data to each pixel value of the second main map data.

[0124] The singular portion extraction process is a process for generating three feature images g21, g22, and g23 in which vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 contained in the first preprocessed image g11 or the second preprocessed image g12 are individually extracted. The three feature images g21, g22, and g23 are the first feature image g21, the second feature image g22, and the third feature image g23.

[0125] The first feature image g21 is an image obtained by extracting a singular portion Ps1 that exists in the first preprocessed image g11 and is not common to the first preprocessed image g11 and the second preprocessed image g12, out of the singular portion Ps1 consisting of one or more significant pixels in the first preprocessed image g11 and the second preprocessed image g12. The first feature image g21 does not include horizontal streaks Ps12 and noise points Ps13, but includes vertical streaks Ps11 if the first preprocessed image g11 includes the vertical streaks Ps11.

[0126] The significant pixels are pixels that can be distinguished from other pixels by comparing each pixel value or an index value based on each pixel value in the test image g1 with a predetermined threshold value.

[0127] The second feature image g22 is an image in which, of the singular portions Ps1 in the first preprocessed image g11 and the second preprocessed image g12, the singular portions Ps1 that exist in the second preprocessed image g12 and are not common to the first preprocessed image g11 and the second preprocessed image g12 are extracted. The second feature image g22 does not include vertical streaks Ps11 and noise points Ps13, but if the second preprocessed image g12 includes horizontal streaks Ps12, the second feature image g22 includes the horizontal streaks Ps12.

[0128] The third feature image g23 is an image in which the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 is extracted. The third feature image g23 does not include the vertical streaks Ps11 and the horizontal streaks Ps12, but includes the noise point Ps13 if the first preprocessed image g11 and the second preprocessed image g12 include the noise point Ps13.

[0129] There are various possible methods for generating the three feature images g21, g22, and g23 from the first preprocessed image g11 and the second preprocessed image g12.

[0130] For example, the feature image generating unit 8c derives the index value Zi by applying the first pixel value Xi, which is each pixel value in the first preprocessed image g11 that exceeds a predetermined reference value, and the second pixel value Yi, which is each pixel value in the second preprocessed image g12 that exceeds the reference value, to the following formula (1), where the subscript i is an identification number of the position of each pixel.

[0131]

number

[0132] The index value Zi of the pixels that make up the vertical streaks Ps11 is a relatively large positive number. The index value Zi of the pixels that make up the horizontal streaks Ps12 is an undetermined small negative number. The index value Zi of the pixels that make up the noise points Ps13 is 0 or a value close to 0. The index value Zi is an example of an index value for the difference between corresponding pixel values ​​in the first preprocessed image g11 and the second preprocessed image g12.

[0133] The above-mentioned properties of the index value Zi can be utilized to simplify the process of extracting vertical streaks Ps11 from the first preprocessed image g11, extracting horizontal streaks Ps12 from the second preprocessed image g12, and extracting noise points Ps13 from the first preprocessed image g11 or the second preprocessed image g12.

[0134] For example, the feature image generating unit 8c generates the first feature image g21 by converting the first pixel value Xi in the first preprocessed image g11 into a first uniqueness Pi derived by the following formula (2). As a result, the first feature image g21 in which the vertical streak Ps11 is extracted from the first preprocessed image g11 is generated.

[0135]

number

[0136] Furthermore, the feature image generating unit 8c generates a second feature image g22 by converting the second pixel value Yi in the second preprocessed image g12 into a second uniqueness Qi derived by the following formula (3). In this way, a second feature image g22 in which the horizontal streak Ps12 is extracted from the second preprocessed image g12 is generated.

[0137]

number

[0138] Furthermore, the feature image generating unit 8c generates a third feature image g23 by converting the first pixel value Xi in the first preprocessed image g11 into a third uniqueness Ri derived by the following formula (4). As a result, the third feature image g23 in which the noise point Ps13 is extracted from the first preprocessed image g11 is generated.

[0139]

number

[0140] Alternatively, the feature image generating unit 8c may generate the third feature image g23 by converting the second pixel value Yi in the second preprocessed image g12 into a third uniqueness Ri derived by the following formula (5). In this way, the third feature image g23 in which the noise point Ps13 is extracted from the second preprocessed image g12 is generated.

[0141]

number

[0142] As described above, the feature image generating unit 8c generates the first feature image g21 by converting each pixel value of the first preprocessed image g11 by the predetermined formula (2) based on the index value Zi. Formula (2) is an example of the first conversion formula.

[0143] Furthermore, the characteristic image generating unit 8c generates a second characteristic image g22 by converting each pixel value of the second preprocessed image g12 by a predetermined formula (3) based on the index value Zi. Formula (3) is an example of the second conversion formula.

[0144] Furthermore, the feature image generating unit 8c generates a third feature image g23 by converting each pixel value of the first preprocessed image g11 or the second preprocessed image g12 by a predetermined formula (4) or (5) based on the index value Zi. Formulas (4) and (5) are each an example of a third conversion formula.

[0145] The process of generating the first feature image g21, the second feature image g22, and the third feature image g23 in step S201 is an example of a process of extracting vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 from one or more singular parts Ps1 in the first preprocessed image g11 and the second preprocessed image g12 as the image defects.

[0146] After the characteristic images g21, g22, and g23 are generated, the characteristic image generating unit 8c shifts the process to step S202.

[0147] <Process S202> In step S202, the singular portion identifying unit 8d identifies the position of the singular portion Ps1 in each of the feature images g21, g22, and g23. The processing in steps S201 and S201 is an example of a processing for identifying the singular portion Ps1 consisting of a plurality of significant pixels in the test image g1.

[0148] For example, the unique portion identifying unit 8d determines that a portion having pixel values ​​outside a predetermined reference range in each of the characteristic images g21, g22, and g23 is the unique portion Ps1.

[0149] In addition, for each of the feature images g21, g22, and g23, if multiple unique parts Ps1 exist within a predetermined proximity range in each of the main scanning direction D1 and the sub-scanning direction D2, the unique part identification unit 8d performs a combining process to combine the multiple unique parts Ps1 into a single series of unique parts Ps1.

[0150] For example, if the first feature image g21 includes two vertical streaks Ps11 spaced apart in the sub-scanning direction D2 within the proximity range, the singular part identification unit 8d combines the two vertical streaks Ps11 into a single vertical streak Ps11 by the combining process.

[0151] Similarly, when the second feature image g22 includes two horizontal streaks Ps12 spaced apart in the main scanning direction D1 within the proximity range, the unique part identification unit 8d combines the two horizontal streaks Ps12 into a single horizontal streak Ps12 by the combining process.

[0152] In addition, when the third feature image g23 includes multiple noise points Ps13 arranged at intervals in the main scanning direction D1 or the sub-scanning direction D2 within the proximity range, the singular part identification unit 8d combines the multiple noise points Ps13 into a single noise point Ps13 by the combining process.

[0153] The singular part identifying unit 8d ends the singular defect determination process when the position of the singular part Ps1 is not identified in any of the three feature images g21, g22, and g23. On the other hand, when the position of the singular part Ps1 is identified in one or more of the three feature images g21, g22, and g23, the singular part identifying unit 8d shifts the process to step S203.

[0154] <Process S203> In step S203, the color vector specifying unit 8e specifies a color vector that represents a vector in color space from one of the color of the unique portion Ps1 in the test image g1 and the color of a reference area including the periphery of the unique portion Ps1 to the other.

[0155] The reference region is a region of a predetermined range determined based on the unique portion Ps1. For example, the reference region is a region that includes a peripheral region adjacent to the unique portion Ps1 and does not include the unique portion Ps1. The reference region may also include the unique portion Ps1 and a peripheral region adjacent to the unique portion Ps1.

[0156] The test image g1 is originally a uniform halftone image. Therefore, when a good test image g1 is formed on the test output sheet 9, the singular portion Ps1 is not identified, and the color vectors at any positions in the test image g1 are generally zero vectors.

[0157] On the other hand, when a unique portion Ps1 is identified, the direction of the color vector between the unique portion Ps1 and the reference area corresponding to the unique portion Ps1 represents an excess or deficiency of toner concentration of any of the four developing colors in the image forming device 2.

[0158] Therefore, the direction of the color vector indicates which of the four image forming units 4x in the image forming apparatus 2 caused the occurrence of the singular portion Ps1.

[0159] The color vector specifying unit 8e may specify, as the color vector, a vector in a color space from one of the color of the singular part Ps1 in the test image g1 and a predetermined reference color to the other. In this case, the reference color is the original color of the test image g1.

[0160] Furthermore, in step S203, the color vector specifying unit 8e determines, based on the color vector, the developed color causing the singular portion Ps1 and the excess or deficiency state of the density of that developed color.

[0161] For example, information on a plurality of unit vectors representing the direction in which the density of each of cyan, magenta, yellow, or black increases and the direction in which the density is insufficient with respect to the reference color of test image g1 is stored in advance in secondary storage device 82.

[0162] The color vector specifying unit 8e normalizes the color vector to a predetermined unit length. Furthermore, the color vector specifying unit 8e determines which of the multiple unit vectors corresponding to the increase or shortage of density of cyan, magenta, yellow, or black the normalized color vector is most similar to, thereby determining the developed color causing the singular portion Ps1 and the excess or shortage of density of the developed color.

[0163] After executing the process of step S203, the color vector specifying unit 8e shifts the process to step S204.

[0164] <Process S204> In step S204, if a singular portion Ps1 is identified in one or both of the second feature image g22 and the third feature image g23, the periodicity determination unit 8f transitions the process to step S205; otherwise, the periodicity determination unit 8f transitions the process to step S206.

[0165] In the following description, one or both of the second feature image g22 and the third feature image g23 in which the peculiar portion Ps1 is identified will be referred to as a periodicity determination target image. The peculiar portion Ps1 in the periodicity determination target image is a horizontal streak Ps12 or a noise point Ps13 (see FIG. 6).

[0166] <Process S205> In step S205, the periodicity determination unit 8f executes a periodic singular part determination process for the periodicity determination target image. The periodic singular part determination process includes a number determination process, a singular part periodicity determination process, and a singular part periodicity cause determination process.

[0167] The number determination process is a process for determining the number of unique portions Ps1 arranged in the sub-scanning direction D2 in the periodicity determination target image.

[0168] Specifically, the periodicity determination unit 8f determines the number of horizontal stripes Ps12 lined up in the sub-scanning direction D2 in the second feature image g22 by counting the number of horizontal stripes Ps12 lined up in the sub-scanning direction D2 whose portion occupying the same range in the main scanning direction D1 exceeds a predetermined ratio.

[0169] Furthermore, the periodicity determination unit 8f determines the number of noise points Ps13 lined up in the sub-scanning direction D2 by counting the number of noise points Ps13 whose positional deviation in the main scanning direction D1 is within a predetermined range and lined up in the sub-scanning direction D2 in the third feature image g23.

[0170] The periodicity determining unit 8f executes the singular portion periodicity determining process only for the singular portions Ps1 of which the number aligned in the sub-scanning direction D2 is two or more.

[0171] Furthermore, the periodicity determining section 8f determines that the singular portion Ps1 having one line in the sub-scanning direction D2 does not have periodicity, and skips the singular portion periodicity determining process and the singular portion periodicity cause determining process.

[0172] The singular part periodicity determination process is a process for determining the presence or absence of one or more predetermined periodicities in the sub-scanning direction D2 for the periodicity determination target image.

[0173] The periodicity corresponds to the outer circumferential length of a rotating body related to image formation, such as the photoconductor 41, charging roller 42a, developing roller 43a, or primary transfer roller 441 in each of the image forming units 4x or in the transfer device 44. The state of the rotating body related to image formation affects the quality of the image formed on the sheet. In the following description, the rotating body related to image formation is referred to as an image forming rotating body.

[0174] When the image defect occurs due to a defect in the imaging rotor, the periodicity corresponding to the outer circumferential length of the imaging rotor may appear as a spacing in the sub-scanning direction D2 between a plurality of horizontal streaks Ps12 or a plurality of noise points Ps13.

[0175] Therefore, when the image for which periodicity is to be determined has a periodicity corresponding to the outer circumferential length of the imaging rotor, it can be said that the imaging rotor corresponding to that periodicity is the cause of the horizontal streaks Ps12 or noise points Ps13 in the image for which periodicity is to be determined.

[0176] When the number of unique portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image is two, the periodicity determination unit 8f executes an interval derivation process as the unique portion periodicity determination process.

[0177] In the interval derivation process, the periodicity determination unit 8f derives the interval between the two unique portions Ps1 in the sub-scanning direction D2 as the period of the two unique portions Ps1.

[0178] When the number of the singular portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image is three or more, the periodicity determination section 8f executes a frequency analysis process as the singular portion periodicity determination process.

[0179] In the frequency analysis process, the periodicity determination unit 8f performs frequency analysis such as a Fourier transform on the periodicity determination target image which includes three or more singular parts Ps1 arranged in the sub-scanning direction D2, thereby identifying the singular part frequency which is the dominant frequency in the frequency distribution of the data sequence of the singular part Ps1 in the periodicity determination target image.

[0180] Furthermore, the periodicity determining unit 8f derives the period corresponding to the singular part frequency as the periods of three or more singular parts Ps1.

[0181] In the singular part periodicity cause determination process, the periodicity determination unit 8f determines whether or not the outer circumferential length of each of the predetermined candidates for the imaging rotor satisfies a predetermined period approximation condition with respect to the period of the singular part Ps1 for each of the predetermined candidates for the imaging rotor in step S205. The plurality of candidates for the imaging rotor in step S205 are an example of the predetermined plurality of cause candidates corresponding to the horizontal streaks Ps12 or the noise points Ps13.

[0182] In the following description, among the singular parts Ps1 contained in the second feature image g22 and the third feature image g23, the singular part Ps1 of the target which is determined to satisfy the periodic approximation condition for any of the candidate imaging rotors is referred to as a periodic singular part, and the other singular parts Ps1 are referred to as non-periodic singular parts.

[0183] The periodicity determination unit 8f determines that one of the imaging rotor candidates determined to satisfy the periodic approximation condition in the singular part periodicity cause determination process is the cause of the periodic singular part, thereby determining the cause of the horizontal streak Ps12 or the noise point Ps13.

[0184] In addition, in step S205, the periodicity determination unit 8f determines, based on the color vector determined in step S203, which of the four image forming units 4x, each having a different development color, is the cause of the horizontal streak Ps12 or the noise point Ps13.

[0185] In addition, when three or more singular parts Ps1 arranged in the sub-scanning direction D2 include a non-periodic singular part that does not correspond to the singular part frequency, the periodicity determination unit 8f targets the non-periodic singular part for the feature pattern recognition processing described below.

[0186] For example, the periodicity determining unit 8f generates inverse Fourier transformed data by performing an inverse Fourier transform on the frequency distribution obtained by the Fourier transform, from which frequency components other than the singular part frequency have been removed.

[0187] Furthermore, the periodicity determination unit 8f determines, among the three or more unique parts Ps1 arranged in the sub-scanning direction D2, the one that is located at a position away from the peak position in the waveform in the sub-scanning direction D2 represented by the inverse Fourier transform data, as the non-periodic unique part.

[0188] Then, when it is determined in step S205 that the second feature image g22 and the third feature image g23 do not include the non-periodic singular portion, the periodicity determining unit 8f terminates the singular defect determination process.

[0189] On the other hand, if it is determined in step S205 that the second feature image g22 and the third feature image g23 include the non-periodic singular portion, the process proceeds to step S206.

[0190] <Process S206> In step S206, the pattern recognition unit 8g performs feature pattern recognition processing on each of the first feature image g21 and the second and third feature images g22 and g23 each including the nonperiodic singular portion. The second feature image g22 including the nonperiodic singular portion or the third feature image g23 including the nonperiodic singular portion is an example of a nonperiodic feature image.

[0191] In the feature pattern recognition process, a first feature image g21 and a second feature image g22 and a third feature image g23 each including the non-periodic singular portion are used as input images. In the feature pattern recognition process, a pattern recognition unit 8g determines whether the input image corresponds to any one of a plurality of predetermined candidate causes corresponding to the image defect by pattern recognition of the input image.

[0192] The input image of the feature pattern recognition process may include the horizontal edge strength map data or the vertical edge strength map data obtained by the edge emphasis filter process. For example, in the feature pattern recognition process for determining a vertical streak Ps11, a first feature image g21 and the horizontal edge strength map data are used as the input image.

[0193] Similarly, in the feature pattern recognition process for determining the horizontal streak Ps12, the second feature image g22 and the vertical edge strength map data are used as the input image.

[0194] Similarly, in the feature pattern recognition process for determining the noise point Ps13, the third feature image g23 and either or both of the horizontal edge strength map data and the vertical edge strength map data are used as the input images.

[0195] For example, the feature pattern recognition process is a process of classifying the input image into one of the multiple candidate causes using a learning model that has been trained in advance using multiple sample images corresponding to the multiple candidate causes as training data.

[0196] For example, the learning model may be a model that employs a classification-type machine learning algorithm called Random Forest, a model that employs a machine learning algorithm called Support Vector Machine (SVM), or a model that employs a Convolutional Neural Network (CNN) algorithm.

[0197] The learning models are prepared individually for the first feature image g21, and the second and third feature images g22 and g23 each including the non-periodic singular portion. In addition, the sample images are used as the teacher data for each of the cause candidates.

[0198] Furthermore, in step S206, the pattern recognition unit 8g determines, based on the color vector determined in step S203, which of the four image forming units 4x, each with a different developing color, is the cause of the vertical streak Ps11, the horizontal streak Ps12, or the noise point Ps13.

[0199] By the process of step S206, the cause of the vertical streak Ps11 and the causes of the horizontal streak Ps12 and noise point Ps13 determined as the non-periodic singular portion are determined. After executing the process of step S206, the pattern recognition unit 8g ends the singular defect determination process.

[0200] [Density unevenness judgment process] Next, an example of the procedure of the density unevenness determination process in step S103 will be described with reference to the flowchart shown in Fig. 5. In the following description, S301, S302, ... represent identification codes of multiple steps in the density unevenness determination process. The density unevenness determination process starts from step S301.

[0201] <Process S301> In step S301, the periodicity determination unit 8f derives a vertical data string VD1 for each predetermined specific color of the test image g1. The specific color is a color corresponding to a developed color of the image forming device 2. The vertical data string VD1 is a data string of representative values ​​V1 of a plurality of pixel values ​​for each line in the main scanning direction D1 in the image of the specific color constituting the test image g1 (see FIG. 8).

[0202] For example, the specific colors are three of the four developing colors of the image forming device 2. In this case, the periodicity determining unit 8f converts the image data of red, green, and blue constituting the test image g1 into image data of cyan, yellow, and magenta.

[0203] Then, the periodicity determination unit 8f derives three vertical data columns VD1 corresponding to cyan, yellow, and magenta by deriving a representative value V1 of multiple pixel values ​​for each line in the main scanning direction D1 for each of the image data of the three specific colors corresponding to the test image g1.

[0204] The specific colors may be the three primary colors of red, green, and blue. In this case, the periodicity determination unit 8f converts each pixel value of the three image data of red, green, and blue in the test image g1 into a value that represents a ratio to the average value or total value of each pixel value of the three image data of red, green, and blue in the test image g1. Furthermore, the periodicity determination unit 8f derives three vertical data strings VD1 for the three image data after conversion.

[0205] Here, red corresponds to cyan, green corresponds to magenta, and blue corresponds to yellow, i.e., cyan density unevenness appears as density unevenness in the converted red image data, magenta density unevenness appears as density unevenness in the converted green image data, and yellow density unevenness appears as density unevenness in the converted blue image data.

[0206] For example, the representative value V1 is the average value, maximum value, or minimum value of the remaining pixel values ​​excluding the pixel values ​​of the singular part Ps1 from all pixel values ​​of the line in the main scanning direction D1. Also, the representative value V1 may be the average value, maximum value, or minimum value of all pixel values ​​of the line in the main scanning direction D1.

[0207] After executing the process of step S301, the periodicity determining unit 8f shifts the process to step S302.

[0208] <Process S302> In step S302, the periodicity determining unit 8f executes periodic unevenness determination processing for the vertical data string VD1 for each specific color.

[0209] For example, the periodicity determining unit 8f performs frequency analysis such as Fourier transform on each vertical data string VD1 to identify the density unevenness frequency that is the dominant frequency in the frequency distribution of the vertical data string VD1.

[0210] Furthermore, the periodicity determining unit 8f derives a period corresponding to the density unevenness frequency as the period of the density unevenness in the test image g1.

[0211] Furthermore, the periodicity determination unit 8f determines whether or not the peripheral length of each of the plurality of predetermined candidates for the imaging rotator satisfies the periodic approximation condition with respect to the period of the density unevenness. If it is determined that any of the plurality of candidates for the imaging rotator satisfies the periodic approximation condition, it means that it is determined that periodic density unevenness occurs in the test image g1.

[0212] The multiple candidates for the imaging rotatable member in step S302 are examples of multiple predetermined candidate causes corresponding to the periodic density unevenness in the test image g1. The periodic density unevenness is an example of an image defect.

[0213] Furthermore, the periodicity determining section 8f determines the cause of the periodic density unevenness based on the developed color corresponding to the vertical data string VD1 and the candidate imaging rotors determined to satisfy the periodic approximation condition.

[0214] However, when the periodic density unevenness occurs due to the black imaging unit 4x, the pixel values ​​vary in all of the red, green, and blue image data constituting the test image g1.

[0215] Therefore, when the periodicity determining section 8f determines that the periodic density unevenness having the same periodicity occurs in all of cyan, magenta, and yellow, it determines that the black imaging section 4x is the cause of the periodic density unevenness.

[0216] If the periodicity determining unit 8f determines that the periodic density unevenness occurs in the test image g1, it ends the density unevenness determining process, and if not, it moves the process to step S303.

[0217] <Process S303> In step S303, the random unevenness determining unit 8h determines whether or not random density unevenness occurs for each of the image data of the three specific colors corresponding to the test image g1. The random density unevenness is one type of the image defect.

[0218] The random unevenness determining unit 8h determines whether or not the variation in pixel value for each of the image data of the three specific colors exceeds a predetermined allowable range, thereby determining whether or not the random density unevenness occurs.

[0219] For example, the magnitude of the variation in pixel values ​​is determined by the variance, standard deviation, or the difference between the median and the maximum and minimum values ​​in each of the image data of the four developed colors.

[0220] However, if the periodicity determining section 8f determines that the random density unevenness occurs in all of cyan, magenta, and yellow, it determines that the black imaging section 4x is the cause of the random density unevenness.

[0221] If the random unevenness determination unit 8h determines that the random density unevenness occurs in the test image g1, it shifts the process to step S304, and if not, it ends the density unevenness determination process.

[0222] <Process S304> In step S304, the pattern recognition unit 8g executes a random pattern recognition process. The random pattern recognition process is a process in which the test image g1 in which the random density unevenness is determined to have occurred is used as an input image, and the input image is subjected to pattern recognition to determine which of one or more of the cause candidates the input image corresponds to.

[0223] After executing the process of step S304, the pattern recognition section 8g ends the density unevenness determination process.

[0224] The execution of the image defect judgment process, which includes the specific defect judgment process and the density unevenness judgment process, by the CPU 80 is an example of an image processing method for determining the cause of the image defect based on a test image g1 read from the output sheet of the image forming device 2.

[0225] As described above, the feature image generating unit 8c generates a first preprocessed image g11 by executing the first preprocessing including the main filtering process with the horizontal direction of the test image g1 as the processing direction Dx1. The main filtering process is a process of converting the pixel value of a pixel of interest Px1 selected sequentially from the test image g1 into the converted value obtained by a process of emphasizing the difference between the pixel value of the region of interest Ax1 and the pixel values ​​of two adjacent regions Ax2 adjacent to both sides of the region of interest Ax1 in the processing direction Dx1 that is preset for the region of interest Ax1 (see step S201 in FIG. 4 and FIG. 6).

[0226] Furthermore, the feature image generation unit 8c generates a second preprocessed image g12 by executing the second preprocessing including the main filtering process with the vertical direction of the test image g1 as the processing direction Dx1 (see step S201 in FIG. 4 and FIG. 6).

[0227] Furthermore, the feature image generating unit 8c extracts vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 from one or more singular portions Ps1 in the first preprocessed image g11 and the second preprocessed image g12 as the image defects (see step S201 in FIG. 4 and FIG. 6). Specifically, in step S201, the feature image generating unit 8c generates a first feature image g21, a second feature image g22, and a third feature image g23.

[0228] The first feature image g21 is an image in which a singular portion Ps1 consisting of multiple significant pixels in the first preprocessed image g11 and the second preprocessed image g12 is extracted, the singular portion Ps1 being present in the first preprocessed image g11 and not common to the first preprocessed image g11 and the second preprocessed image g12 (see Figure 6).

[0229] The second feature image g22 is an image in which the unique portion Ps1 in the first preprocessed image g11 and the second preprocessed image g12 is extracted, that is, the unique portion Ps1 that exists in the second preprocessed image g12 and is not common to the first preprocessed image g11 and the second preprocessed image g12 (see FIG. 6).

[0230] The third feature image g23 is an image in which the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 is extracted (see FIG. 6).

[0231] The process of step S201 is an example of a process for generating a plurality of feature images g21, g22, and g23 by performing a predetermined feature extraction process on a test image g1. The feature extraction process includes the first pre-processing, the second pre-processing, and the singular part extraction process.

[0232] The feature extraction process is a simple process with a small computational load. By using such a simple process, three feature images g21, g22, and g23, in which the unique portions Ps1 having different shapes are individually extracted from one test image g1, can be generated.

[0233] Then, the periodicity determination unit 8f and the pattern recognition unit 8g execute the periodic singular part determination process and the feature pattern recognition process using the first feature image g21, the second feature image g22, and the third feature image g23 to determine the causes of the vertical streaks Ps11, the horizontal streaks Ps12, and the noise points Ps13, which are types of image defects (see steps S205 and S206 in Figure 4 and Figure 6).

[0234] The cause of the image defect is determined individually for the three feature images g21, g22, and g23, each of which includes a different type of singular portion Ps1, so that the cause of the image defect can be determined with high accuracy by a relatively simple determination process.

[0235] The periodic singular part determination process in step S205 is a process that determines the presence or absence of one or more predetermined periodicities in the sub-scanning direction D2 for the second feature image g22 or the third feature image g23, and determines the cause of the horizontal streak Ps12 or the noise point Ps13 depending on the result of the periodicity determination.

[0236] If horizontal streaks Ps12 or noise points Ps13 occur due to a defect in the rotating body related to image formation, the cause of the horizontal streaks Ps12 or noise points Ps13 can be determined with high accuracy by the periodic singular part determination process, which determines the periodicity corresponding to the outer circumferential length of the rotating body.

[0237] The feature pattern recognition process in step S206 is a process for determining, by pattern recognition of an input image, which of a plurality of predetermined candidate causes corresponding to the vertical streaks, the horizontal streaks, and the noise points the input image corresponds to. Here, the first feature image g21 and, among the second feature image g22 and the third feature image g23, those determined to have no periodicity by the periodic singular part determination process are the input images in step S206 (see steps S204 to S206 in FIG. 4).

[0238] The periodic singular part determination process in step S205 and the feature pattern recognition process in step S206 are an example of a predetermined cause determination process using the first feature image g21, the second feature image g22, and the third feature image g23.

[0239] The feature pattern recognition process using a learning model or the like is performed for each of the feature images g21, g22, and g23 from which a specific type of singular portion Ps1 has been extracted. This makes it possible to determine the cause of the image defect with high accuracy while suppressing the amount of calculation by the CPU 80. Furthermore, the learning model for each type of singular portion Ps1 can be sufficiently trained by simply preparing a relatively small amount of teacher data corresponding to each specific type of singular portion Ps1.

[0240] Moreover, the feature pattern recognition process in step S206 is executed on the first feature image g21, which was not the target of the periodic singular part determination process in step S205, among the multiple feature images g21, g22, and g23, and on the second feature image g22 or the third feature image g23, which were determined to have no periodicity by the periodic singular part determination process in step S205 (see steps S204 to S206 in FIG. 4).

[0241] In the above case, in the feature pattern recognition process, it is possible to exclude the possibility that the cause of the image defect is due to the periodicity of the rotating body related to the image formation, which further simplifies the feature pattern recognition process.

[0242] In addition, the color vector identification unit 8e identifies the color vector that represents a vector in color space from one of the colors of the unique portion Ps1 in the test image g1 and the color of the reference area including the periphery of the unique portion Ps1 to the other (see step S203 in FIG. 4).

[0243] Then, the periodicity determination unit 8f in step S205 and the pattern recognition unit 8g in step S206 further use the color vector in the cause determination processing to determine the causes of the vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13. That is, the periodicity determination unit 8f and the pattern recognition unit 8g determine, based on the color vector, which of the multiple imaging units 4x corresponding to the multiple developing colors in the image forming apparatus 2 is the cause of the image defect.

[0244] In the image forming apparatus 2 capable of printing color images, the color vector is used to easily and reliably determine which of a plurality of developed colors the image defect is caused by. Furthermore, the cause of the image defect can be determined based on the test output sheets 9, the number of which is less than the total number of developed colors of the image forming apparatus 2.

[0245] Furthermore, the periodicity determination unit 8f executes the periodic unevenness determination process for each of the predetermined specific colors for the test image g1 (see steps S301 and S302 in FIG. 5). The periodic unevenness determination process is a process for determining the presence or absence of one or more predetermined periodicities in the sub-scanning direction D2, and further determining the presence or absence and cause of the periodic density unevenness, which is a type of image defect, according to the determination result of the periodicity.

[0246] The periodic unevenness determination process makes it possible to determine the cause of the periodic density unevenness with high accuracy.

[0247] Further, the random unevenness determination unit 8h determines whether the random density unevenness occurs by determining whether the variation in pixel value for each specific color exceeds a predetermined allowable range for the test image g1 determined to have no periodicity by the periodic unevenness determination process in step S302 (see step S303 in FIG. 5). The random density unevenness is one type of image defect.

[0248] Furthermore, the pattern recognition unit 8g executes the random pattern recognition process using the test image g1, in which it has been determined that the random density unevenness has occurred, as the input image (see step S304 in FIG. 5). In the random pattern recognition process, it is determined by pattern recognition of the input image whether the input image corresponds to one or more of the cause candidates.

[0249] Moreover, the test image g1 is a mixed-color halftone image obtained by combining a plurality of uniform single-color halftone images corresponding to a plurality of developed colors in the image forming device 2. This allows the CPU 80 to determine the cause of the image defects for all developed colors in the image forming device 2 by using the test image g1 which is smaller than the number of developed colors used by the image forming device 2.

[0250] [First application example] Next, the procedure of the feature image generating process in the first application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0251] In the following description, S401, S402, ... represent identification codes of a plurality of steps in the feature image generation processing according to this application example. The feature image generation processing according to this application example starts from step S401.

[0252] <Process S401> In step S401, the characteristic image generating unit 8c selects a compression rate to be adopted from a plurality of preset compression rate candidates, and moves the process to step S402.

[0253] <Process S402> In step S402, the characteristic image generating unit 8c compresses the scanned image at the selected compression ratio to generate a test image g1. The processes in steps S401 and S402 are an example of a compression process. After that, the characteristic image generating unit 8c shifts the process to step S403.

[0254] <Process S403> In step S403, the feature image generating unit 8c generates a first preprocessed image g11 by performing the first preprocessing on the compressed test image g1 obtained in step S401. After that, the feature image generating unit 8c shifts the process to step S404.

[0255] <Process S404> In step S404, the feature image generating unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the compressed test image g1 obtained in step S401. After that, the feature image generating unit 8c shifts the process to step S405.

[0256] <Process S405> In step S405, if the processes of steps S401 to S404 have been performed for all of the multiple compression ratio candidates, the characteristic image generation unit 8c transitions to step S406; if not, it performs the processes of steps S401 to S404 for different compression ratios.

[0257] In the compression processing in steps S401 and S402, the characteristic image generating unit 8c generates a plurality of test images g1 having different sizes by compressing the read image at a plurality of compression ratios.

[0258] Furthermore, in steps S403 and S404, the characteristic image generation unit 8c performs the first pre-processing and the second pre-processing on a plurality of test images g1 to generate a plurality of first pre-processed images g11 and a plurality of second pre-processed images g12, each of which corresponds to the plurality of test images g1.

[0259] <Process S406> In step S406, the feature image generating unit 8c executes the singular part extraction process on each of the first preprocessed images g11 and the second preprocessed images. As a result, the feature image generating unit 8c generates multiple candidates for the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the test images g1. After that, the feature image generating unit 8c shifts the process to step S407.

[0260] <Process S407> In step S407, the feature image generating unit 8c generates a first feature image g21, a second feature image g22, and a third feature image g23 by aggregating the candidates obtained in step S406. Thereafter, the feature image generating unit 8c ends the feature image generation process.

[0261] For example, the feature image generating unit 8c sets a representative value such as the maximum or average value of each pixel value in a plurality of candidates for the first feature image g21 as each pixel value of the first feature image g21. The same applies to the second feature image g22 and the third feature image g23.

[0262] The processes in steps S401 to S404 are an example of a process for generating a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12 by performing the first preprocessing and the second preprocessing multiple times with different size ratios between the test image g1 and the attention area Ax1 and the adjacent area Ax2. Changing the compression rate is an example of changing the size ratio between the test image g1 and the attention area Ax1 and the adjacent area Ax2.

[0263] Furthermore, the processing of steps S406 to S407 is an example of a process for generating a first feature image g21, a second feature image g22, and a third feature image g23 by the singular part extraction process based on a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12.

[0264] By employing this application example, vertical streaks Ps11 or horizontal streaks Ps12 of different thicknesses, or noise points Ps13 of different sizes can be extracted without omission.

[0265] [Second application example] Next, the procedure of the feature image generating process in the second application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0266] In the following description, S501, S502, ... represent identification codes of a plurality of steps in the feature image generating process according to this application example. The feature image generating process according to this application example starts from step S501.

[0267] <Process S501~S505> The characteristic image generating unit 8c executes the processes of steps S501 to S505, which are the same processes as steps S401 to S405. In step S505, when the processes of steps S501 to S504 have been executed for all of the multiple compression rate candidates, the characteristic image generating unit 8c shifts the process to step S506.

[0268] <Process S506> In step S506, the feature image generating unit 8c aggregates the plurality of first preprocessed images g11 and the plurality of second preprocessed images into one each. After that, the feature image generating unit 8c shifts the process to step S507.

[0269] For example, the feature image generating unit 8c sets a representative value such as a maximum value or an average value of each pixel value in the multiple first preprocessed images g11 as each pixel value of the aggregated first feature image g21. The same applies to the multiple second preprocessed images g12.

[0270] <Process S507> In step S507, the feature image generating unit 8c performs the singular part extraction process on the aggregated first preprocessed image g11 and second preprocessed image g12 to generate a first feature image g21, a second feature image g22, and a third feature image g23. Thereafter, the feature image generating unit 8c ends the feature image generation process.

[0271] When this application example is adopted, the same effects as when the first application example is adopted can be obtained.

[0272] [Third application example] Next, the procedure of the feature image generating process in the third application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0273] In the following description, S601, S602, ... represent identification codes of a plurality of steps in the feature image generation processing according to this application example. The feature image generation processing according to this application example starts from step S601.

[0274] In the following description, the sizes of the region of interest Ax1 and the adjacent region Ax2 in the first pre-processing and the second pre-processing are referred to as filter sizes.

[0275] <Process S601> In step S601, the characteristic image generating unit 8c selects the filter size to be adopted from a plurality of preset size candidates, and moves the process to step S602.

[0276] <Process S602> In step S602, the feature image generating unit 8c generates a first preprocessed image g11 by performing the first preprocessing with the filter size selected in step S601 on the test image g1. After that, the feature image generating unit 8c shifts the process to step S603.

[0277] <Process S603> In step S603, the feature image generating unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the test image g1 with the filter size selected in step S601. After that, the feature image generating unit 8c shifts the process to step S604.

[0278] <Process S604> In step S604, if the processes of steps S601 to S603 have been performed for all of the multiple size candidates, the characteristic image generating unit 8c transitions to step S605; if not, it performs the processes of steps S601 to S603 using different filter sizes.

[0279] In steps S601 to S604, the feature image generating unit 8c performs the first pre-processing multiple times and the second pre-processing multiple times on one test image g1 with different sizes of the attention area Ax1 and the adjacent area Ax2, thereby generating a plurality of first pre-processed images g11 and a plurality of second pre-processed images g12.

[0280] <Process S605, S606> In steps S605 and S606, the feature image generating unit 8c executes the same processes as steps S406 and S407 in Fig. 9. Thereafter, the feature image generating unit 8c ends the feature image generating process.

[0281] By the processing of steps S605 and S606, multiple candidates for the first feature image g21, the second feature image g22, and the third feature image g23 are aggregated, and the aggregated first feature image g21, the second feature image g22, and the third feature image g23 are generated.

[0282] The processes in steps S601 to S604 are an example of a process of generating a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12 by performing the first preprocessing and the second preprocessing multiple times with different size ratios between the test image g1 and the attention area Ax1 and the adjacent area Ax2. Changing the filter size is an example of changing the size ratio between the test image g1 and the attention area Ax1 and the adjacent area Ax2.

[0283] By employing this application example, vertical streaks Ps11 or horizontal streaks Ps12 of different thicknesses, or noise points Ps13 of different sizes can be extracted without omission.

[0284] [Fourth application example] Next, the procedure of the feature image generating process in the fourth application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0285] In the following description, S701, S702, ... represent identification codes of a plurality of steps in the feature image generating process according to this application example. The feature image generating process according to this application example starts from step S701.

[0286] <Process S701~S704> The characteristic image generating unit 8c executes the processes of steps S701 to S704, which are the same processes as steps S601 to S604. In step S704, when the processes of steps S701 to S703 have been executed for all of the plurality of size candidates, the characteristic image generating unit 8c shifts the process to step S705.

[0287] <Process S705, S706> Furthermore, the characteristic image generating unit 8c executes the processes of steps S705 and S706, which are the same processes as steps S506 and S507. Thereafter, the characteristic image generating unit 8c ends the characteristic image generating process.

[0288] When this application example is adopted, the same effects as when the first application example is adopted can be obtained.

[0289] [Fifth application example] Next, the feature image generating process in the fifth application example of the image processing device 10 will be described.

[0290] In this application example, the feature image generation unit 8c distinguishes between pixels that constitute the singular portion Ps1 and pixels that do not by comparing each pixel value of the first preprocessed image g11 and the second preprocessed image g12 with a predetermined reference range.

[0291] That is, in this application example, the characteristic image generating unit 8c specifies the singular portion Ps1 based on the magnitude of each pixel value of the first preprocessed image g11 and the second preprocessed image g12 in the singular portion extraction process.

[0292] Furthermore, the feature image generating unit 8c extracts vertical streaks Ps11 from the singular portion Ps1 of the first preprocessed image g11 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12.

[0293] Furthermore, the feature image generating unit 8c extracts horizontal streaks Ps12 from the unique portion Ps1 of the second preprocessed image g12 by removing the unique portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12.

[0294] Furthermore, the feature image generating unit 8c extracts a singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 as a noise point Ps13.

[0295] For example, the characteristic image generating unit 8c generates a first characteristic image g21 by converting the first pixel value Xi determined to be other than the vertical streak Ps11 in the first preprocessed image g11 into an interpolated value based on the surrounding pixel values.

[0296] Similarly, the characteristic image generating unit 8c generates a second characteristic image g22 by converting the second pixel values ​​Yi determined to be other than the horizontal streak Ps12 in the second preprocessed image g12 into interpolated values ​​based on the surrounding pixel values.

[0297] Similarly, the feature image generating unit 8c generates a third feature image g23 by converting the first pixel value Xi determined to be other than the noise point Ps13 in the first preprocessed image g11 into an interpolated value based on the surrounding pixel values.

[0298] Alternatively, the feature image generating unit 8c may generate the third feature image g23 by converting the second pixel value Yi determined to be other than the noise point Ps13 in the second preprocessed image g12 into an interpolated value based on the surrounding pixel values.

[0299] [Sixth Application Example] The image defect determination process in the sixth application example of the image processing device 10 will be described below.

[0300] In general, it may be difficult for an image sensor to accurately detect the shade of the yellow part in an image in which yellow and other colors are mixed, depending on the density of each color.Similarly, it may be difficult for an image sensor to accurately detect the shade of the colored part in an image in which black and colored colors are mixed, depending on the density of each color.

[0301] In this application example, the job control section 8b causes two or three test output sheets 9, on which different types of original test images g01 are formed, to be output in the test print process.

[0302] When three test output sheets 9 are output, one sheet is formed with an original mixed color test image formed by combining a uniform cyan monochrome halftone image and a uniform magenta monochrome halftone image, another sheet is formed with an original yellow test image which is a uniform yellow monochrome halftone image, and another sheet is formed with an original gray test image which is a uniform black monochrome halftone image.

[0303] When two test output sheets 9 are output, one sheet has a mixed color test image formed by combining a uniform cyan monochrome halftone image, a uniform magenta monochrome halftone image, and a uniform yellow monochrome halftone image, and the other sheet has the original gray test image formed thereon.

[0304] Therefore, the test image g1 in this application example includes a mixed color test image, a yellow test image, and a gray test image corresponding to the original mixed color test image, the original yellow test image, and the original gray test image, respectively.

[0305] The yellow test image and the gray test image are halftone images of one developed color different from the color mixed into the mixed-color test image. The yellow test image and the gray test image are each an example of a single-color test image.

[0306] In this application example, the characteristic image generating unit 8c generates a first characteristic image g21, a second characteristic image g22, and a third characteristic image g23 for each of the mixed color test image and the single color test image read from the multiple test output sheets 9.

[0307] Furthermore, in this application example, the unique portion identifying unit 8d identifies the position of the unique portion Ps1 for the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the mixed-color test image and the monochromatic test image, respectively. Note that the mixed test image and the monochromatic test image in this application example are each an example of a target test image for identifying the unique portion Ps1.

[0308] In this application example, the color vector determination unit 8e, the periodicity determination unit 8f, and the pattern recognition unit 8g execute processing to determine the cause of the image defect using the first characteristic image g21, the second characteristic image g22, and the third characteristic image g23 corresponding to the color mixing test image, as in the above embodiment.

[0309] In this application example, the periodicity judgment unit 8f and the pattern recognition unit 8g judge the cause of the image defect corresponding to the singular portion in the color mixing test image from among a plurality of image forming units 4x corresponding to a plurality of developing colors mixed in the color mixing test image.

[0310] Furthermore, in this application example, the periodicity judgment unit 8f and the pattern recognition unit 8g, similar to the above embodiment, perform processing to determine the cause of the image defect using the first characteristic image g21, the second characteristic image g22, and the third characteristic image g23 corresponding to the monochrome test image.

[0311] The periodicity determination unit 8f and the pattern recognition unit 8g in this application example determine the cause of the image defect corresponding to the singular portion in the monochrome test image from one of the multiple image forming units 4x that corresponds to the color of the monochrome test image.

[0312] When this application example is adopted, it is also possible to determine the cause of the image defect based on the test output sheets 9 which are fewer in number than the total number of developing colors of the image forming apparatus 2 .

[0313] [7th Application Example] The image defect determination process in the seventh application example of the image processing device 10 will be described below.

[0314] In this application example, the CPU 80 determines the cause of the image defect by other processing, instead of the processing by the pattern recognition unit 8g.

[0315] For example, the CPU 80 in this application example determines the cause of the image defect by making a case distinction based on one or more of the number of unique parts Ps1 in the first feature image g21, the second feature image g22, or the third feature image g23, the thickness or aspect ratio of the unique part Ps1, and the level of the pixel value of the unique part Ps1. [Explanation of symbols]

[0316] 1: Image reader 2: Image forming device 4: Printing section 4x: Imaging section 8: Data processing section 9: Test output sheet 10: Image processing device 80: CPU 81: RAM 82 :Secondary storage device Ps1: Singular part Ps11: Vertical stripes Ps12: Horizontal stripes Ps13: Noise point Px1: Pixel of interest g1: Test image g11: First preprocessed image g12: Second preprocessed image g21: First feature image g22: Second feature image g23: Third feature image

Claims

1. An image processing method in which a processor determines a cause of an image defect based on a mixed-color test image in which a plurality of single-color halftone images corresponding to a plurality of developed colors obtained through an image reading process for an output sheet of an image forming apparatus are combined, the method comprising the steps of: The processor identifies a singular portion of a plurality of significant pixels in the color mixing test image; The processor identifies a color vector representing a vector in a color space from one of the color of the singular part in the color mixing test image and the color of a reference area including a periphery of the singular part to the other; and determining, by the processor, which of a plurality of image forming units corresponding to the plurality of developing colors in the image forming apparatus is the cause of the image defect based on the color vector identified from one of the color mixing test images.

2. The processor identifies the unique portion in the single-color test image read from a sheet on which the single-color test image, which is a halftone image of one developed color different from the color mixed in the mixed-color test image, is formed; The image processing method according to claim 1 , further comprising the processor determining a cause of the image defect corresponding to the singular portion in the monochrome test image from among the plurality of image forming units that correspond to the color of the monochrome test image.

3. the plurality of developing colors mixed into the mixed color test image include cyan and magenta; 3. The image processing method of claim 2, wherein one of the developing colors included in the single color test image is yellow or black.

4. The processor identifies the singular portion in the target test image, which is the color mixing test image, generating a first preprocessed image by executing a first preprocessing including a main filter process for converting pixel values ​​of pixels of interest selected sequentially from the test image into converted values ​​obtained by a process for emphasizing a difference between a pixel value of a region of interest including the pixel of interest and pixel values ​​of two adjacent regions adjacent to both sides of the region of interest in a processing direction that is preset for the region of interest, with the horizontal direction of the test image as the processing direction; generating a second preprocessed image by performing a second preprocessing including the main filtering process with the vertical direction of the test image as the processing direction; the processor executes a peculiar portion extraction process to extract, as the image defect, a first peculiar portion that is present in the first preprocessed image and is not common to the first preprocessed image and the second preprocessed image, a second peculiar portion that is present in the second preprocessed image and is not common to the first preprocessed image and the second preprocessed image, and a third peculiar portion that is common to the first preprocessed image and the second preprocessed image, from among peculiar portions consisting of one or more significant pixels in the first preprocessed image and the second preprocessed image, The processor determining a cause of the image defect comprises: The image processing method according to claim 1 , further comprising: determining, by the processor, a cause of the image defect for each of the first unique portion, the second unique portion, and the third unique portion individually.

5. The first pretreatment is generating first primary map data by performing the primary filtering process with the horizontal direction as the processing direction; generating lateral edge strength map data by performing edge enhancement filtering on the test image with the lateral direction as the processing direction and the region of interest and one of the two adjacent regions as targets; generating the first pre-processed image by correcting each pixel value of the first main map data with each pixel value of the corresponding horizontal edge strength map data; The second pretreatment is generating second primary map data by performing the primary filtering process with the vertical direction as the processing direction; generating vertical edge strength map data by performing the edge enhancement filter process on the test image with the vertical direction as the processing direction and targeting the region of interest and the two adjacent regions; 5. The image processing method of claim 4, further comprising: generating the second preprocessed image by correcting each pixel value of the second main map data with each pixel value of the corresponding vertical edge strength map data.

6. generating a first feature image from which the first unique portion is extracted, a second feature image from which the second unique portion is extracted, and a third feature image from which the third unique portion is extracted; a periodic singular portion determination process for determining the presence or absence of one or more predetermined periodicities in the vertical direction for the second feature image or the third feature image, and determining a cause of the second singular portion or the third singular portion according to a result of the periodicity determination; 6. The image processing method according to claim 4, further comprising: a feature pattern recognition process for using, as input images, the first feature image and images in which the second singular portion and the third singular portion that are determined to have no periodicity by the periodic singular portion determination process are extracted, and determining, by pattern recognition of the input images, which of a plurality of predetermined cause candidates corresponding to the first singular portion, the second singular portion, and the third singular portion the input image corresponds to.

7. 7. The image processing method according to claim 6, wherein the feature pattern recognition process is a process of classifying the input image into one of the plurality of cause candidate candidates by a learning model that has been trained in advance using a plurality of sample images corresponding to the plurality of cause candidate images as teacher data.

8. The image processing method according to any one of claims 1 to 7, further comprising: the processor determining whether or not one or more predetermined periodicities exist in a predetermined sub-scanning direction for each predetermined color of a target test image, which is the mixed-color test image; and executing a periodic unevenness determination process to determine the occurrence and cause of periodic density unevenness, which is a type of image defect, based on the result of the periodicity determination.

9. The image processing method according to claim 8, further comprising the processor determining whether or not random density unevenness, which is a type of image defect, has occurred by determining whether or not a variation in pixel values ​​for each predetermined color exceeds a predetermined tolerance range for the target test image that has been determined to have no periodicity by the periodic unevenness determination process.

10. The image processing method according to claim 9, further comprising: the processor performing a random pattern recognition process in which the target test image in which the random density unevenness has been determined to have occurred is used as an input image, and the processor performs a pattern recognition process to determine whether the input image corresponds to any one or more predetermined candidate causes of the image defect through pattern recognition of the input image.

11. An image processing device comprising a processor for executing the image processing method according to any one of claims 1 to 10.

Images