Image processing method and image processing device

Abstract

To correctly determine the presence or absence and the cause of a periodic lateral stripe being one type of an image defect based on a test image obtained through image reading processing on an output sheet from an image forming apparatus.SOLUTION: A processor 80 creates an object image g22 obtained by extracting a lateral stripe Ps12 extending in a lateral direction in a test image g1. In addition, the processor 80 determines the presence or absence of one or more predetermined periodicities in the vertical direction for the object image g22, and determines the cause of the lateral stripe Ps12 according to a result of determination of the periodicities.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an image processing method and an image processing device 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 printing process to form an image on a sheet. During the printing process, image defects such as vertical streaks, horizontal streaks, noise spots, or density unevenness may occur in the image formed on the output sheet.

[0003] For example, if the image forming apparatus is an apparatus that performs the printing process by electrophotography, the cause of the image defect may be various parts such as the photosensitive member, the charging unit, the developing unit, the transfer unit, etc. Skill is required to determine the cause of the image defect.

[0004] It is also known that in image processing devices, the phenomenon that causes vertical streaks, which are an example of the image defect, and characteristic information such as the color, density, or screen ruling of the vertical streaks are associated in advance as table data, and the phenomenon that causes the vertical streaks is identified based on the information on the color, density, or screen ruling 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, or screen ruling are set by threshold values ​​for each type of phenomenon that causes the vertical stripes. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-83544 Summary of the Invention [Problem to be solved by the invention]

[0007] Incidentally, horizontal streaks, which are one type of image defect, may be caused by a defect in one of the multiple rotating bodies in the imaging unit of the image forming apparatus, which include a photosensitive body on whose surface an electrostatic latent image is formed, a charging roller that charges the surface of the photosensitive body, and a developing roller that develops the electrostatic latent image into a toner image.

[0008] When a rotation defect occurs in the rotating body, periodic horizontal stripes tend to appear in the output image, and the periodic horizontal stripes appear in the output image aligned vertically at a period corresponding to the circumferential length of the rotating body where the rotation defect occurs.

[0009] An object of the present invention is to provide an image processing method and an image processing device that can accurately determine the presence or absence and cause of periodic horizontal streaks, which are a type of image defect, based on a test image obtained through an image reading process on an output sheet of an image forming device. [Means for solving the problem]

[0010] An image processing method according to one aspect of the present invention is a method for determining image defects in a test image obtained through an image reading process for an output sheet of an image forming apparatus. The image processing method includes the processor generating a target image from which horizontal stripes extending horizontally in the test image are extracted. The image processing method further includes the processor determining the presence or absence of one or more predetermined periodicities in the vertical direction in the target image, and determining the cause of the horizontal stripes based on the periodicity determination result.

[0011] An image processing device according to another aspect of the present invention includes a processor that executes the steps of the image processing method. [Effects of the Invention]

[0012] According to the present invention, it is possible to provide an image processing method and an image processing device that can correctly determine the presence or absence and cause of periodic horizontal streaks, which are a type of image defect, based on a test image obtained through an image reading process on an output sheet of an image forming device. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating the configuration of an image processing apparatus according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing the configuration of a data processing unit in the image processing apparatus according to the embodiment. [Figure 3] FIG. 3 is a flowchart showing an example of the procedure of the image defect determination process in the image processing apparatus according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of the procedure of the specific defect determination process in the image processing apparatus according to the embodiment. [Figure 5] FIG. 5 is a diagram showing an example of a test image including a singular portion, and examples of a preprocessed image and a feature image generated based on the test image. [Figure 6] FIG. 6 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 7] FIG. 7 is a flowchart showing an example of the procedure of the feature image generation process in the first application example of the image processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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 does not limit the technical scope of the present invention.

[0015] [Configuration of image processing device 10] The image processing device 10 according to the embodiment includes an image forming device 2 that executes a print process. The print process is a process of forming an image on a sheet. The sheet is an image forming medium such as paper or a sheet-like resin member.

[0016] The image processing device 10 further includes an image reading device 1 that executes a reading process to read 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.

[0017] The image to be printed is an image read from the original by the image reading device 1 or an image represented by print data received from a host device (not shown). The host device is an information processing device such as a personal computer or a mobile information terminal.

[0018] Furthermore, the image forming apparatus 2 may form a predetermined original test image g01 on the sheet by the print process (see FIG. 5). The original test image g01 is an image that serves as the basis for a test image g1 that is used to determine the presence or absence of an image defect and the cause of the defect in the image forming apparatus 2 (see FIG. 5). The test image g1 will be described later.

[0019] 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.

[0020] 1, the image forming apparatus 2 includes a sheet transport mechanism 3 and a printing unit 4. The sheet transport mechanism 3 includes a sheet sending mechanism 31 and a plurality of pairs of sheet transport rollers 32.

[0021] The sheet feeding mechanism 31 feeds the sheet from the sheet storage unit 21 to the sheet conveying path 30. A plurality of pairs of sheet conveying rollers 32 convey the sheet along the sheet conveying path 30 and discharge the sheet on which an image has been formed onto the discharge tray 22.

[0022] The printing unit 4 performs the printing process on the sheet conveyed by the sheet conveying mechanism 3. In this embodiment, the printing unit 4 performs the printing process by an electrophotographic method.

[0023] The printing 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 photosensitive member 41, a charging device 42, a developing device 43, and a drum cleaning device 45.

[0024] The photoreceptor 41 rotates, and the charging device 42 uniformly charges the surface of the photoreceptor 41. The charging device 42 includes a charging roller 42a that rotates while in contact with the surface of the photoreceptor 41. A charging voltage is applied to the charging roller 42a, which charges the surface of the photoreceptor 41.

[0025] The laser scanning unit 4y writes an electrostatic latent image on the charged surface of the photosensitive member 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 develops the electrostatic latent image into a toner image by supplying toner to the photoreceptor 41. The transfer device 44 transfers the toner image on the surface of the photoreceptor 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 that can perform the printing process for color images. To this end, 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 photosensitive members 41, an intermediate transfer belt 440, a secondary transfer roller 442, and a belt cleaning device 443.

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

[0031] In each image forming unit 4x, a primary transfer roller 441 rotates and urges the intermediate transfer belt 440 toward the surface of the photosensitive member 41. The primary transfer roller 441 transfers the toner image from the photosensitive member 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 4x, the drum cleaning device 45 removes and collects toner remaining on the photoconductor 41 without being transferred to the intermediate transfer belt 440 from the photoconductor 41.

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

[0034] The belt cleaning device 443 removes and collects the toner remaining on the intermediate transfer belt 440 without being transferred to the sheet.

[0035] 1, the image processing apparatus 10 includes an image forming apparatus 2 and an image reading apparatus 1, as well as 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 user operations. 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 provided to the user.

[0038] As shown in FIG. 2, the data processing unit 8 includes a CPU (Central Processing Unit) 80, a RAM (Random Access Memory) 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, performing various 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 also be realized by other types of processors, 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 via a network such as a LAN (Local Area Network). The CPU 80 performs all data transmission and reception with 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 may be used 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 that is output and referenced by the CPU 80 during the execution of the programs.

[0043] The CPU 80 includes a plurality of processing modules implemented by executing a computer program. The plurality of processing modules includes a main control unit 8a and a job control unit 8b. Note that some or all of the plurality of processing modules may be implemented 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 content 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 FIG. 5).

[0048] As described above, the image forming apparatus 2 performs the printing process by electrophotography. In this case, the cause of the image defect can be thought of as various parts such as the photosensitive member 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 to form a predetermined original test image g01 on the sheet.

[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 displays a predetermined guidance message on the display unit 802. 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 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 will be described later, the CPU 80 executes a process to determine the presence or absence of the image defect and its cause based on the read image or a test image g1, which is an image obtained by compressing the read image (see FIG. 5). 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 original test image g01 from the test output sheet 9 may be, for example, a digital camera. The process in which the image reading device 1 or the digital camera reads the original test image g01 from the test output sheet 9 is an example of an image reading process for the test output sheet 9.

[0055] Incidentally, density unevenness, which is one type of image defect, may be caused by rotation failure of one of the multiple rotating bodies in the image forming unit 4x of the image forming device 2. The multiple rotating bodies include the photosensitive body 41, the charging roller 42a, the developing roller 43a, and the primary transfer roller 441. Hereinafter, these rotating bodies related to image formation will be referred to as image forming rotating bodies.

[0056] When the rotation of the imaging rotor is improper, periodic horizontal stripes tend to appear in the output image, and the periodic horizontal stripes appear in the output image in a vertical line at a period corresponding to the circumferential length of the imaging rotor where the rotation is improper.

[0057] In the image processing device 10, the CPU 80 executes the image defect determination process described later (see FIG. 3), which allows the CPU 80 to correctly determine the cause of the periodic horizontal streaks based on the test image g1 obtained through the image reading process on the test output sheet 9.

[0058] The images processed by the CPU 80, such as the test image g1, are digital image data. The digital image data constitutes map data containing 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.

[0059] 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.

[0060] For example, original test image g01 and test image g1 are mixed-color halftone images formed by combining multiple uniform monochromatic halftone images corresponding to multiple development colors in image forming device 2. Each of the multiple monochromatic halftone images is an image formed uniformly at a predetermined intermediate gradation reference density.

[0061] In this embodiment, the original test image g01 and the test image g1 are mixed-color halftone images formed by combining four uniform single-color halftone images corresponding to all the developed colors in the image forming device 2. In the test print process, one test output sheet 9 containing 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. The test image g1 in this embodiment is an example of a mixed-color test image.

[0062] In addition, the multiple processing modules in the 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, and a pattern recognition unit 8g in order to execute the image defect determination process (see Figure 2).

[0063] [Image defect detection processing] An example of the procedure for 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 for multiple steps in the image defect determination process.

[0064] 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 process.

[0065] <Process S101> In step S101, the characteristic image generating unit 8c generates a test image g1 from the read image obtained by the image reading process on the test output sheet 9.

[0066] For example, the characteristic image generating unit 8c extracts a portion of the original image excluding the outer margin area from the scanned image as a test image g1.

[0067] Alternatively, the feature image generating unit 8c generates a test image g1 by performing a compression process to compress the original image portion, excluding the outer margin area, from the scanned image to a predetermined standard resolution. The feature image generating unit 8c compresses the scanned image if 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.

[0068] <Process S102> In step S102, the feature image generating unit 8c starts the 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 peculiar part Ps1 (see FIG. 5). The peculiar part Ps1 is an example of the image defect.

[0069] Furthermore, when the specific defect determination process is completed, the main control unit 8a shifts the process to step S103.

[0070] <Process S103> In step S103, the periodicity determining unit 8f starts a density unevenness determining process, which will be described later. Furthermore, when the density unevenness determining process is completed, the main control unit 8a shifts the process to step S104.

[0071] <Process S104> In step S104, if the main control unit 8a determines that the image defect has occurred through the processing in step S102 or step S103, it shifts the processing to step S105, and if not, it shifts the processing to step S106.

[0072] <Process S105> In step S105, the main control unit 8a executes a defect handling process that is previously associated with the type and cause of the image defect that is determined to have occurred in the process of step S102 or step S103.

[0073] For example, the defect response processing includes one or both of the first response processing and the second response processing shown below. The first response processing is processing for displaying a message on the display unit 802 to prompt replacement of the part that is causing the image defect. The second response processing is processing for correcting image creation parameters to eliminate or mitigate the image defect. The image creation parameters are parameters related to the control of the image forming unit 4x.

[0074] After executing the defect handling process, the main control unit 8a ends the image defect determination process.

[0075] <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.

[0076] [Singular defect detection process] 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.

[0077] <Process S201> First, in step S201, the feature image generating unit 8c performs a predetermined feature extraction process on the test image g1 to generate a plurality of feature images g21, g22, and g23. Each of the feature images g21, g22, and g23 is an image in which a specific type of predetermined unique portion Ps1 is extracted from the test image g1.

[0078] In this embodiment, the plurality of 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. 5).

[0079] The first feature image g21 is an image in which the vertical streak Ps11 in the test image g1 is extracted. The second feature image g22 is an image in which the horizontal streak Ps12 in the test image g1 is extracted. The third feature image g23 is an image in which the noise point Ps13 in the test image g1 is extracted.

[0080] In this embodiment, the feature extraction process includes a first pre-processing, a second pre-processing, and a singular part extraction process. In the following description, a pixel sequentially selected from the test image g1 is referred to as a target pixel Px1 (see FIG. 5).

[0081] The feature image generating unit 8c generates a first preprocessed image g11 by performing the first preprocessing on the test image g1 with the main scanning direction D1 as the processing direction Dx1 (see FIG. 5).

[0082] Furthermore, the feature image generating unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the test image g1 with the sub-scanning direction D2 as the processing direction Dx1 (see FIG. 5).

[0083] 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.

[0084] The first pre-processing includes a main filtering process in which the processing direction Dx1 is the main scanning direction D1. The main filtering process converts the pixel values ​​of target pixels Px1 sequentially selected from the test image g1 into converted 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 FIG. 5).

[0085] 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 the region of interest Ax1 on both sides in a processing direction Dx1 that is set in advance. The region of interest Ax1 and the adjacent regions Ax2 each include one or more pixels.

[0086] The sizes of the region of interest Ax1 and the adjacent region Ax2 are set according to the width of the vertical streak Ps11 or horizontal streak Ps12 to be extracted, or the size of the noise point Ps13 to be extracted.

[0087] The region of interest Ax1 and the adjacent region Ax2 each occupy the same range in a direction intersecting the processing direction Dx1. In the example shown in FIG. 6, the region of interest Ax1 is a region of 21 pixels spanning three columns and seven rows, centered around the pixel of interest Px1. Each of the adjacent regions Ax2 is also a region of 21 pixels spanning three columns and seven 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 a direction intersecting the processing direction Dx1. The sizes of the region of interest Ax1 and the adjacent region Ax2 are set in advance.

[0088] In the main filtering 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.

[0089] For example, the first correction coefficient K1 is a coefficient of 1 or greater 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.

[0090] The feature image generating unit 8c multiplies each pixel value of the target area Ax1 by a first correction coefficient K1 to derive the first correction value corresponding to each pixel of the target area Ax1, and multiplies each pixel value of the two adjacent areas Ax2 by a second correction coefficient K2 to derive the second correction value corresponding to each pixel of the two adjacent areas Ax2.The feature image generating unit 8c then combines the first correction value and the second correction value to derive the converted value of the pixel value of the target pixel Px1.

[0091] For example, the feature image generation unit 8c derives the integrated 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.

[0092] The absolute value of the conversion value is a value obtained by amplifying the absolute value of the difference between the pixel values ​​of the region of interest Ax1 and the pixel values ​​of the two adjacent regions Ax2. The process of deriving the conversion value by integrating the first correction value and the second correction value is an example of a process of emphasizing the difference between the pixel values ​​of the region of interest Ax1 and the pixel values ​​of the two adjacent regions Ax2.

[0093] It is also possible that the first correction coefficient K1 is a negative number and the second correction coefficient K2 is a positive number.

[0094] 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 integrated values ​​obtained by the main filtering process with the main scanning direction D1 as the processing direction Dx1.

[0095] As shown in Figure 5, when test image g1 contains one or both of vertical streaks Ps11 and noise points Ps13, the first main map data, in which one or both of vertical streaks Ps11 and noise points Ps13 contained 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] For example, the feature image generating unit 8c may generate, 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.

[0100] As shown in Figure 5, 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 with the sub-scanning direction D2 as the processing direction Dx1.

[0101] 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 the processing direction Dx1.

[0102] However, in the main filtering process, an erroneous integrated value, whose sign is opposite to the integrated value representing the actual state of the singular portion Ps1, may be derived at the edge portions at both ends of the singular portion Ps1 in the processing direction Dx1. If such an erroneous integrated value is processed as a pixel value representing the singular portion Ps1, it may adversely affect the determination of the image defect.

[0103] 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.

[0104] Similarly, the second pre-processing further includes the edge enhancement 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.

[0105] The edge enhancement filter process is a process for performing edge enhancement on the region of interest Ax1 and one of two adjacent regions Ax2 that is predetermined.

[0106] 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 is the combination of 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, which has the opposite positive and negative sign to the third correction coefficient K3 (see Figure 5).

[0107] 5, 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 the value obtained by multiplying the third correction coefficient K3 by the number of pixels in the region of interest Ax1 and the value obtained by multiplying the fourth correction coefficient K4 by the number of pixels in one of the adjacent regions Ax2 is zero.

[0108] The edge emphasis filtering 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.

[0109] Similarly, the edge emphasis filtering process is performed with the sub-scanning direction D2 as the processing direction Dx1, whereby vertical edge intensity map data is generated in which each pixel value of the test image g1 is converted into the edge intensity.

[0110] In this embodiment, the feature image generating unit 8c generates the first main map data by the main filtering process in which the main scanning direction D1 is set as the processing direction Dx1.

[0111] 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.

[0112] Furthermore, the feature 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 feature 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.

[0113] Similarly, the feature 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.

[0114] Furthermore, the characteristic image generating unit 8c generates the vertical edge intensity map data by executing the edge emphasis filter process with the sub-scanning direction D2 as the processing direction Dx1.

[0115] 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.

[0116] The singular part extraction process generates three feature images g21, g22, and g23 by individually extracting vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 contained in the first preprocessed image g11 or the second preprocessed image g12. 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.

[0117] The first feature image g21 is an image in which, of the singular portions Ps1 consisting of one or more significant pixels in the first preprocessed image g11 and the second preprocessed image g12, singular portions Ps1 that exist in the first preprocessed image g11 but are not common to the first preprocessed image g11 and the second preprocessed image g12 are extracted. The first feature image g21 does not include horizontal streaks Ps12 or noise points Ps13, but if the first preprocessed image g11 includes vertical streaks Ps11, the first feature image g21 includes the vertical streaks Ps11.

[0118] 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.

[0119] 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, singular portions Ps1 that exist in the second preprocessed image g12 but 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 or noise points Ps13, but if the second preprocessed image g12 includes horizontal streaks Ps12, the second feature image g22 includes the horizontal streaks Ps12.

[0120] 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 if the first preprocessed image g11 and the second preprocessed image g12 include the noise point Ps13, the third feature image g23 also includes the noise point Ps13.

[0121] 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.

[0122] 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 equation (1): where the subscript i is an identification number for the position of each pixel.

[0123]

number

[0124] The index value Zi of the pixels that make up the vertical streak Ps11 is a relatively large positive number. The index value Zi of the pixels that make up the horizontal streak Ps12 is an unspecified small negative number. The index value Zi of the pixels that make up the noise point 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.

[0125] The above 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.

[0126] 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 equation (2). As a result, the first feature image g21 is generated in which the vertical streak Ps11 is extracted from the first preprocessed image g11.

[0127]

number

[0128] 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 equation (3). In this way, a second feature image g22 is generated in which the horizontal streak Ps12 is extracted from the second preprocessed image g12.

[0129]

number

[0130] 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 equation (4). In this way, the third feature image g23 is generated in which the noise point Ps13 is extracted from the first preprocessed image g11.

[0131]

number

[0132] 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 equation (5). In this way, the third feature image g23 is generated in which the noise point Ps13 is extracted from the second preprocessed image g12.

[0133]

number

[0134] 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 using the predetermined equation (2) based on the index value Zi. Equation (2) is an example of the first conversion equation.

[0135] Furthermore, the feature image generating unit 8c generates a second feature image g22 by converting each pixel value of the second preprocessed image g12 using a predetermined formula (3) based on the index value Zi. Formula (3) is an example of the second conversion formula.

[0136] 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 using predetermined equation (4) or (5) based on the index value Zi. Equations (4) and (5) are each an example of a third conversion equation.

[0137] 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 generating an image in which the vertical streaks Ps11, the horizontal streaks Ps12, and the noise points Ps13 in the test image g1 are each individually extracted.

[0138] After the characteristic images g21, g22, and g23 are generated, the characteristic image generating unit 8c shifts the process to step S202.

[0139] <Process S202> In step S202, the unique portion identifying unit 8d identifies the position of the unique portion Ps1 in each of the characteristic images g21, g22, and g23.

[0140] For example, the unique part identifying unit 8d determines that a part in each of the characteristic images g21, g22, and g23 that has a pixel value outside a predetermined reference range is the unique part Ps1.

[0141] In other words, the unique portion identifying unit 8d identifies, as a unique portion Ps1, a portion where the uniquenesses Pi, Qi, and Ri based on the pixel values ​​in the first preprocessed image g11 and the second preprocessed image g12 fall outside the reference range. The uniquenesses Pi, Qi, and Ri are examples of converted values ​​based on the pixel values ​​in the first preprocessed image g11 and the second preprocessed image g12. The processing by the feature image generating unit 8c in this embodiment is an example of processing to identify a unique portion Ps1 consisting of one or more significant pixels in the test image g1.

[0142] In addition, for each of the characteristic 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.

[0143] 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 unique part identification unit 8d combines the two vertical streaks Ps11 into a single vertical streak Ps11 through the combining process.

[0144] Similarly, if the second feature image g22 includes two horizontal stripes Ps12 spaced apart in the main scanning direction D1 within the proximity range, the unique part identification unit 8d combines the two horizontal stripes Ps12 into a single horizontal stripe Ps12 through the combining process.

[0145] Furthermore, 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.

[0146] The unique part identifying unit 8d ends the unique defect determination process when the position of the unique 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 unique part Ps1 is identified in one or more of the three feature images g21, g22, and g23, the unique part identifying unit 8d transitions the process to step S203.

[0147] <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 the reference area including the periphery of the unique portion Ps1 to the other.

[0148] The reference area is a predetermined range determined based on the unique portion Ps1. For example, the reference area is an area that includes a surrounding area adjacent to the unique portion Ps1 but does not include the unique portion Ps1. Alternatively, the reference area may include the unique portion Ps1 and a surrounding area adjacent to the unique portion Ps1.

[0149] 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 position in the test image g1 are generally zero vectors.

[0150] On the other hand, when the unique part Ps1 is identified, the direction of the color vector between the unique part Ps1 and the reference area corresponding to that unique part Ps1 represents an excess or deficiency of toner concentration of one of the four developing colors in the image forming device 2.

[0151] 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.

[0152] The color vector specifying unit 8e may specify, as the color vector, a vector in color space from one of the color of the singular portion Ps1 in the test image g1 and a predetermined reference color to the other, in which case the reference color is the original color of the test image g1.

[0153] Furthermore, in step S203, the color vector specifying unit 8e determines, based on the color vector, the developed color that is the cause of the unique portion Ps1 and whether the density of that developed color is excessive or insufficient.

[0154] 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 relative to the reference color of test image g1 is stored in advance in secondary storage device 82.

[0155] 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 an increase or deficiency in the density of cyan, magenta, yellow, or black the normalized color vector most closely resembles, thereby determining the developed color causing the singular portion Ps1 and whether the developed color is excessive or insufficient in density.

[0156] After executing the process of step S203, the color vector specifying unit 8e shifts the process to step S204.

[0157] <Process S204> In step S204, if the peculiar part Ps1 is identified in one or both of the second feature image g22 and the third feature image g23, the periodicity determination unit 8f shifts the processing to step S205; otherwise, it shifts the processing to step S206.

[0158] 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 the 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. 5).

[0159] <Process S205> In step S205, the periodicity determination unit 8f executes a periodic singular part determination process on 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.

[0160] The number determination process is a process for determining the number of unique portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image.

[0161] Specifically, the periodicity determination unit 8f determines the number of horizontal stripes Ps12 aligned in the sub-scanning direction D2 by counting the number of horizontal stripes Ps12 aligned in the sub-scanning direction D2 in the second feature image g22 whose portion occupying the same range in the main scanning direction D1 exceeds a predetermined ratio.

[0162] 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 lined up in the sub-scanning direction D2 in the third feature image g23 whose positional deviation in the main scanning direction D1 is within a predetermined range.

[0163] 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.

[0164] Furthermore, the periodicity determining unit 8f determines that the singular portion Ps1, which is aligned in the sub-scanning direction D2 in one piece, does not have periodicity, and skips the singular portion periodicity determining process and the singular portion periodicity cause determining process.

[0165] The singular portion periodicity determination process is a process for determining whether or not the image to be subjected to periodicity determination has one or more predetermined periodicities in the sub-scanning direction D2. The periodicities correspond to the outer circumferential length of the image forming rotor. The condition of the image forming rotor affects the quality of the image formed on the sheet.

[0166] If 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 the spacing in the sub-scanning direction D2 between multiple horizontal streaks Ps12 or multiple noise points Ps13.

[0167] Therefore, if the image to be subjected to periodicity determination has a periodicity corresponding to the outer peripheral length of the imaging rotor, it can be said that the imaging rotor corresponding to that periodicity is the cause of the horizontal streak Ps12 or noise point Ps13 in the image to be subjected to periodicity determination.

[0168] 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.

[0169] 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.

[0170] If the number of unique portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image is three or more, the periodicity determination unit 8f executes frequency analysis processing as the unique portion periodicity determination processing.

[0171] In the frequency analysis process, the periodicity determination unit 8f performs frequency analysis such as a Fourier transform on the image to be subjected to periodicity determination, which includes three or more singular parts Ps1 arranged in the sub-scanning direction D2, to identify the singular part frequency, which is the dominant frequency in the frequency distribution of the data sequence of the singular part Ps1 in the image to be subjected to periodicity determination.

[0172] Furthermore, the periodicity determining unit 8f derives the period corresponding to the singular part frequency as the period of three or more singular parts Ps1.

[0173] When the second feature image g22 is the image to be subjected to periodicity determination, the periodicity determination unit 8f derives vertical data strings for each of the colors red, green, and blue for the second feature image g22. The vertical data strings are data strings of representative values ​​of multiple pixel values ​​for each line in the main scanning direction D1 in the second feature image g22.

[0174] The representative value corresponds to the horizontal representative value. The second feature image g22 is an example of a target image from which horizontal stripes Ps12 extending horizontally in the test image g1 have been extracted.

[0175] For example, the periodicity determination unit 8f identifies a horizontal streak range, which is the range that the horizontal streak g22 occupies in the main scanning direction D1 of the second feature image g22. Furthermore, the periodicity determination unit 8f derives the vertical data string consisting of data on the representative values ​​of multiple pixel values ​​within the horizontal streak range for each line in the main scanning direction D1 of the second feature image g22.

[0176] Furthermore, the periodicity determining unit 8f performs the frequency analysis on the vertical data string to determine whether or not the vertical data string has periodicity.

[0177] In the singular part periodicity cause determination process, the periodicity determination unit 8f determines whether the outer periphery of each of the predetermined plurality of candidates for the imaging rotor satisfies a predetermined periodic approximation condition with respect to the period of the singular part Ps1. The plurality of candidates for the imaging rotor in step S205 are an example of the predetermined plurality of candidate causes corresponding to the horizontal streaks Ps12 or the noise points Ps13.

[0178] 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 that is determined to satisfy the periodic approximation condition for any of the candidate imaging rotors will be referred to as a periodic singular part, and the other singular parts Ps1 will be referred to as non-periodic singular parts.

[0179] The periodicity determination unit 8f determines that one of the imaging rotor candidates determined to satisfy the periodic approximation condition in the singular portion periodicity cause determination process is the cause of the periodic singular portion, thereby determining the cause of the horizontal streak Ps12 or the noise point Ps13.

[0180] Also, 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 with a different development color, is the cause of the horizontal streak Ps12 or noise point Ps13.

[0181] The process for determining the cause of periodicity of the singular part when the second feature image g22 is the image to be determined for periodicity is an example of a process for determining whether or not one or more predetermined periodicities exist in the vertical direction for the second feature image g22, and determining the cause of the horizontal streak Ps12 based on the result of the periodicity determination.

[0182] Furthermore, if 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 subjects the non-periodic singular part to the feature pattern recognition processing described below.

[0183] 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.

[0184] Furthermore, the periodicity determination unit 8f determines that, among the three or more unique parts Ps1 arranged in the sub-scanning direction D2, the one that exists at a position away from the peak position in the waveform in the sub-scanning direction D2 indicated by the inverse Fourier transform data is the non-periodic unique part.

[0185] Then, when it is determined as a result of the processing 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 ends the singular defect determination processing.

[0186] 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.

[0187] <Process S206> In step S206, the pattern recognition unit 8g performs feature pattern recognition processing on the first feature image g21 and the second and third feature images g22 and g23, each of which includes the aperiodic singular portion. The second feature image g22 including the aperiodic singular portion and the third feature image g23 including the aperiodic singular portion are examples of aperiodic feature images.

[0188] In the feature pattern recognition process, a first feature image g21 and a second and third feature images g22 and g23 each including the non-periodic singular portion are used as input images. In the feature pattern recognition process, a pattern recognition unit 8g recognizes the pattern of the input images and determines which of a plurality of predetermined candidate causes of the image defect the input image corresponds to.

[0189] Furthermore, the input image for the feature pattern recognition process may include the horizontal edge strength map data or the vertical edge strength map data obtained by the edge enhancement 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.

[0190] Similarly, in the feature pattern recognition process for determining horizontal streaks Ps12, the second feature image g22 and the vertical edge strength map data are used as the input image.

[0191] 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.

[0192] 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.

[0193] 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 SVM (Support Vector Machine), or a model that employs a CNN (Convolutional Neural Network) algorithm.

[0194] The learning models are individually prepared for the first feature image g21 and the second and third feature images g22 and g23, each of which includes the non-periodic singular portion. Furthermore, the plurality of sample images are used as the training data for each of the cause candidates.

[0195] Also, 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 development color, is the cause of the vertical streak Ps11, horizontal streak Ps12, or noise point Ps13.

[0196] By the processing 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 processing of step S206, the pattern recognition unit 8g ends the singular defect determination processing.

[0197] The execution of the image defect judgment process, including the specific defect judgment process, by the CPU 80 is an example of an image processing method for determining the cause of the image defect based on the test image g1 obtained through the image reading process for the output sheet of the image forming device 2.

[0198] As described above, the feature image generating unit 8c generates a first preprocessed image g11 by performing 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 converts the pixel values ​​of the target pixels Px1 sequentially selected from the test image g1 into converted values ​​obtained by processing that emphasizes the difference between the pixel values ​​of the target region Ax1 and the pixel values ​​of two adjacent regions Ax2 adjacent to both sides of the target region Ax1 in the processing direction Dx1 that is preset for the target region Ax1 (see step S201 in FIG. 4 and FIG. 6).

[0199] Furthermore, the feature image generation unit 8c generates a second preprocessed image g12 by performing the second preprocessing, which includes the main filtering process, with the vertical direction of the test image g1 as the processing direction Dx1 (see step S201 in Figure 4 and Figure 6).

[0200] Furthermore, the characteristic image generating unit 8c extracts vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 from one or more unique portions Ps1 in the first preprocessed image g11 and the second preprocessed image g12 as the image defects (see step S201 in Figure 4 and Figure 6).

[0201] The feature extraction process in step S201 is a simple process with a small computational load. By using this simple process, three feature images g21, g22, and g23, each having a different shape, can be generated from a single test image g1, with unique portions Ps1 individually extracted.

[0202] The process in step S201 in which the feature image generating unit 8c generates the second feature image g22 is an example of a process for generating a target image in which the horizontal streaks Ps12 extending horizontally in the test image g1 are extracted.

[0203] 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, respectively (see steps S205 and S206 in Figure 4 and Figure 6).

[0204] The cause of the image defect is determined individually for the three feature images g21, g22, and g23, each of which contains a different type of unique portion Ps1, so that the cause of the image defect can be determined with high accuracy through a relatively simple determination process.

[0205] The periodic singular part determination process in step S205 is a process that determines whether or not one or more predetermined periodicities exist 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 based on the result of the periodicity determination.

[0206] The process in step S205 includes the periodic singular part determination process in which the second feature image g22 is used as the target image for periodicity determination. This process is an example of a process in which the presence or absence of one or more predetermined periodicities in the vertical direction of the target image is determined, and the cause of the horizontal streaks Ps12 is determined based on the periodicity determination result.

[0207] The periodic singular part determination process, which determines the periodicity corresponding to the outer peripheral length of the imaging rotor, makes it possible to determine with high accuracy the cause of periodic horizontal streaks Ps12 or periodic noise points Ps13 that occur due to defects in the imaging rotor.

[0208] 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 part Ps1 in the test image g1 and the color of the reference area including the periphery of the unique part Ps1 to the other (see step S203 in Figure 4).

[0209] Then, in the cause determination process, the periodicity determination unit 8f in step S205 and the pattern recognition unit 8g in step S206 further use the color vector 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 use the color vector to determine which of the multiple development colors in the image forming device 2 corresponds to the cause of the image defect.

[0210] In an image forming device 2 capable of printing color images, the color vector is used to easily and reliably determine which of the multiple developed colors the image defect is caused by.

[0211] [First application example] Next, the procedure of the feature image generation process in the first application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0212] 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.

[0213] <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.

[0214] <Process S402> In step S402, the characteristic image generating unit 8c compresses the scanned image at the selected compression rate to generate a test image g1. The processes in steps S401 and S402 are an example of a compression process. Thereafter, the characteristic image generating unit 8c proceeds to step S403.

[0215] <Process S403> In step S403, the feature image generation unit 8c generates a first preprocessed image g11 by performing the first preprocessing on the compressed test image g1 obtained in step S401. Thereafter, the feature image generation unit 8c shifts the process to step S404.

[0216] <Process S404> In step S404, the feature image generation unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the compressed test image g1 obtained in step S401. Thereafter, the feature image generation unit 8c shifts the process to step S405.

[0217] <Process S405> In step S405, if the processing of steps S401 to S404 has been performed for all of the multiple compression rate candidates, the feature image generation unit 8c transitions the processing to step S406; if not, it performs the processing of steps S401 to S404 for different compression rates.

[0218] In the compression processing of steps S401 and S402, the characteristic image generating unit 8c generates a plurality of test images g1 of different sizes by compressing the read image at a plurality of compression rates.

[0219] Furthermore, in steps S403 and S404, the feature image generation unit 8c performs the first preprocessing and the second preprocessing on multiple test images g1 to generate multiple first preprocessed images g11 and multiple second preprocessed images g12, each corresponding to the multiple test images g1.

[0220] <Process S406> In step S406, the feature image generation 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 generation 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. Thereafter, the feature image generation unit 8c proceeds to step S407.

[0221] <Process S407> In step S407, the feature image generation 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 generation unit 8c ends the feature image generation process.

[0222] For example, the feature image generating unit 8c sets a representative value, such as the maximum or average value, of each pixel value in the multiple 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.

[0223] The processing in steps S401 to S404 is an example of processing 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 region of interest Ax1 and the adjacent region Ax2. Changing the compression rate is an example of changing the size ratio between the test image g1 and the region of interest Ax1 and the adjacent region Ax2.

[0224] Furthermore, the processing of steps S406 to S407 is an example of processing for generating a first feature image g21, a second feature image g22, and a third feature image g23 by the singular part extraction processing based on a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12.

[0225] By adopting this application example, it is possible to extract all vertical streaks Ps11 or horizontal streaks Ps12 of different thicknesses, or noise points Ps13 of different sizes.

[0226] The first preprocessing and the second preprocessing in this application example 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 size of the region of interest Ax1 and the adjacent region Ax2.

[0227] In addition, the feature image generating unit 8c may generate a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12 by changing the filter size and executing the first preprocessing and the second preprocessing multiple times.

[0228] The filter size is the size of the region of interest Ax1 and the adjacent region Ax2 in the first pre-processing and the second pre-processing. Changing the filter size is an example of changing the size ratio between the test image g1 and the size of the region of interest Ax1 and the adjacent region Ax2.

[0229] In this application example, the feature image generating unit 8c may also aggregate the plurality of first preprocessed images g11 and the plurality of second preprocessed images into one image.

[0230] For example, the feature image generating unit 8c sets a representative value, such as the maximum or average value of each pixel value in the plurality of first preprocessed images g11, as each pixel value of the aggregated first feature image g21. The same applies to the plurality of second preprocessed images g12.

[0231] Furthermore, the feature image generating unit 8c may perform the singular part extraction process on the aggregated first preprocessed image g11 and second preprocessed image g12, thereby generating a first feature image g21, a second feature image g22, and a third feature image g23.

[0232] [Second application example] Next, the feature image generation process in the second application example of the image processing device 10 will be described.

[0233] In this application example, the feature image generating unit 8c identifies, as the unique portion Ps1, a portion in which the pixel values ​​in the first preprocessed image g11 and the second preprocessed image g12 are outside a predetermined reference range.

[0234] That is, in this application example, the feature image generation unit 8c, in the singular portion extraction process, identifies the singular portion Ps1 based on the magnitude of each pixel value in the first preprocessed image g11 and the second preprocessed image g12. The process by the feature image generation unit 8c in this application example is an example of a process for identifying the singular portion Ps1 consisting of one or more significant pixels in the test image g1.

[0235] Furthermore, the characteristic image generating unit 8c extracts vertical streaks Ps11 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 from the singular portion Ps1 of the first preprocessed image g11.

[0236] Furthermore, the characteristic image generating unit 8c extracts horizontal streaks Ps12 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 from the singular portion Ps1 of the second preprocessed image g12.

[0237] 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.

[0238] For example, the feature image generating unit 8c generates a first feature 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.

[0239] Similarly, the feature image generating unit 8c generates a second feature 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.

[0240] 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.

[0241] 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.

[0242] [Third application example] The image defect determination process in the third application example of the image processing device 10 will be described below.

[0243] In general, it can be difficult for an image sensor to accurately detect the shade of the yellow portion of an image in which yellow and other colors are mixed, depending on the density of each color.Similarly, it can be difficult for an image sensor to accurately detect the shade of the colored portion of an image in which black and other colors are mixed, depending on the density of each color.

[0244] In this application example, the job control section 8b outputs two or three test output sheets 9, each having a different type of original test image g01 formed thereon, in the test print process.

[0245] 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.

[0246] When two test output sheets 9 are output, one sheet is formed with 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 is formed with the original gray test image.

[0247] Therefore, the test image g1 in this application example includes a color mixture test image, a yellow test image, and a gray test image corresponding to the original color mixture test image, the original yellow test image, and the original gray test image, respectively.

[0248] The yellow test image and the gray test image are each halftone images of a single developed color that is 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.

[0249] In this application example, the feature image generation unit 8c generates a first feature image g21, a second feature image g22, and a third feature image g23 for each of the mixed-color test image and the single-color test image read from multiple test output sheets 9.

[0250] Furthermore, in this application example, the unique portion identifying unit 8d identifies the position of the unique portion Ps1 in 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 single-color test image, respectively. Note that the mixed-color test image and the single-color test image in this application example are each an example of a target test image for identifying the unique portion Ps1.

[0251] In this application example, the color vector identification unit 8e, the periodicity determination unit 8f, and the pattern recognition unit 8g perform processing to determine the cause of the image defect using the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the color mixing test image, as in the above embodiment.

[0252] In this application example, the periodicity determination unit 8f and the pattern recognition unit 8g determine the cause of the image defect corresponding to the unique part in the color mixing test image from among the multiple image forming units 4x corresponding to the multiple developing colors mixed in the color mixing test image.

[0253] Furthermore, in this application example, the periodicity determination unit 8f and the pattern recognition unit 8g, as in the above embodiment, perform processing to determine the cause of the image defect using the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the monochromatic test image.

[0254] In this application example, the periodicity determination unit 8f and the pattern recognition unit 8g determine the cause of the image defect corresponding to the unique 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.

[0255] 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 colors developed by the image forming apparatus 2.

[0256] [Fourth application example] The image defect determination process in the fourth application example of the image processing device 10 will be described below.

[0257] In this application example, the CPU 80 determines the cause of the singular part Ps1 by other processing instead of the processing by the pattern recognition unit 8g.

[0258] For example, in this application example, the CPU 80 determines the cause of the peculiar part Ps1 by case classification based on one or more of the number of peculiar 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 peculiar part Ps1, and the level of the pixel value of the peculiar part Ps1.

[0259] [5th ​​application example] Next, the feature image generation process in the fifth application example of the image processing device 10 will be described.

[0260] In this application example, the feature image generation unit 8c distinguishes between pixels that constitute the unique 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.

[0261] That is, in this application example, the characteristic image generating unit 8c identifies the unique portion Ps1 based on the magnitude of each pixel value of the first preprocessed image g11 and the second preprocessed image g12 in the unique portion extraction process.

[0262] Furthermore, the characteristic image generating unit 8c extracts vertical streaks Ps11 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 from the singular portion Ps1 of the first preprocessed image g11.

[0263] Furthermore, the characteristic image generating unit 8c extracts horizontal streaks Ps12 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 from the singular portion Ps1 of the second preprocessed image g12.

[0264] 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.

[0265] For example, the feature image generating unit 8c generates a first feature 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.

[0266] Similarly, the feature image generating unit 8c generates a second feature 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.

[0267] 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.

[0268] 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. [Explanation of symbols]

[0269] 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. 1. An image processing method in which a processor judges image defects in a test image obtained through an image reading process for an output sheet of an image forming apparatus, comprising: generating a target image in which horizontal streaks extending in a horizontal direction in the test image are extracted by the processor; The processor determines whether or not one or more predetermined periodicities exist in the vertical direction of the target image, and determines a cause of the horizontal streaks according to a result of the periodicity determination. The processor generating the target image includes: 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; generating, as the target image, an image in which singular areas consisting of one or more significant pixels in the first preprocessed image and the second preprocessed image that are present in the second preprocessed image and are not common to the first preprocessed image and the second preprocessed image are extracted.

2. 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 horizontal direction as the processing direction and targeting the region of interest and the two adjacent regions; 2. The image processing method of claim 1, 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.

3. The processor determining whether or not the periodicity exists includes: Deriving a vertical data string consisting of horizontal representative value data that is a representative value of a plurality of pixel values ​​for each line along the horizontal direction in the target image; 3. The image processing method according to claim 1, further comprising: determining, by the processor, the presence or absence of periodicity in the vertical data string by performing a frequency analysis on the vertical data string.

4. The processor deriving the vertical data sequence comprises: A horizontal streak range is identified, which is a range that the horizontal streak occupies in the horizontal direction of the target image. And, deriving, for each line along the horizontal direction in the target image, the vertical data string made up of data of the horizontal representative value which is a representative value of a plurality of pixel values ​​in the horizontal streak range.

5. An image processing device comprising a processor for executing the image processing method according to any one of claims 1 to 4.

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