JP2017191294A - Image processing apparatus, control method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor capable of accurately detecting abnormal images due to scratches on a drum.SOLUTION: An image diagnostic part 126 in an MFP (Multifunction Peripheral) 101 divides an image into plural areas in a conveyance direction of a recording material, calculates a maximum value or a minimum value of brightness in a direction perpendicular to a conveyance direction for each of the areas, and generates a piece of signal data in which the maximum values or minimum values are arranged in order in a conveyance direction. When a dot having a large difference from pixels in the vicinity appears repeatedly at period of a peripheral length of a photoreceptor drum in at least one area, the image imaging diagnostic part 126 determines that there is a scratch on a drum based on the generated signal data.SELECTED DRAWING: Figure 4

Description

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

  In recent years, machines (image processing apparatuses such as printers) that have achieved image quality equivalent to that of printing machines have appeared along with improvements in performance of electrophotographic apparatuses. Maintaining high image quality is essential in order to operate in the same way as a printing press, but the printer deteriorates due to stressful usage, and an image (abnormal image) that is different from the normal image that should be output is output. “Image quality problems” (image defects) may occur. An abnormal image generated due to such deterioration is difficult to automatically detect using a sensor or the like, and there are many cases in which an abnormal image is dealt with after receiving an indication from the user.

  The electrophotographic apparatus is configured to include a large number of image forming members made of a rotating body, and if any of the image forming members deteriorates, an abnormal image may be generated with a period of the peripheral length. In particular, if the usage is stressed for a long time, the surface of the electrophotographic photosensitive drum (photosensitive drum) is locally damaged due to abrasion with the drum cleaner (hereinafter referred to as drum scratch).

  A portion where the surface of the photosensitive drum is damaged is charged differently from a predetermined potential. As a result, when an image is printed, an abnormal image is generated there. Further, the density of the abnormal image appearing in the image on the recording material also changes depending on the degree of scratches on the surface of the photosensitive drum. For example, when the surface of the photosensitive drum is scratched, the charge amount is larger than a predetermined potential. As a result, the post-exposure potential is higher than that of the other portions, so that a smaller amount of toner is developed than the other portions during development. Then, when the image is printed, an abnormal image having a light density such that the image data is lost is output.

  As wear with the drum cleaner further progresses, the surface of the photosensitive drum is deeply damaged, and the charge amount becomes smaller than a predetermined potential. As a result, the post-exposure potential is lower than that in other portions, so that a larger amount of toner is developed during development than in other portions. Then, when the image is printed, a dark abnormal image close to a solid image to which image data that is not originally printed is added is output. That is, when an abnormal image due to a drum scratch occurs, image data desired by the user cannot be obtained, and it is necessary to deal with parts replacement.

  Patent Document 1 discloses an image analysis apparatus that detects an image defect occurrence interval based on a predetermined edge position of projection waveform data subjected to addition processing in the main scanning direction of image data.

JP 2008-139500 A

  Density fluctuations (hereinafter referred to as density unevenness) that appear in a period of the peripheral length of the photosensitive drum are phenomena having periodicity, similar to drum scratches. In the case of density unevenness, density fluctuations occur in the image data, but the image data desired by the user is not lost. On the other hand, in the case of a drum scratch, image data desired by the user cannot be obtained. Therefore, in order to obtain image data desired by the user, it is necessary to specify an abnormal image caused by a drum scratch.

  However, when an abnormal image is specified by the method of Patent Document 1, the failure location is estimated from the periodicity of the peripheral length of each image forming member, so that the detection is affected by density unevenness that is not a detection target (analysis target). The detection accuracy of the abnormal image due to the target drum scratch is reduced.

  An object of the present invention is to provide an image processing apparatus that can detect an abnormal image caused by a drum scratch with high accuracy.

  An image processing apparatus according to an embodiment of the present invention includes a dividing unit that divides an image into a plurality of areas in the conveyance direction of the recording material, and the maximum luminance of pixels located in a direction perpendicular to the conveyance direction for each of the areas. A generation unit that obtains a value or a minimum value and generates first data in which the maximum value or the minimum value is arranged in the transport direction; and at least one of the regions based on the first data Determination means for determining that a drum flaw is present when a point having a large difference is repeatedly detected at a period of the peripheral length of the photosensitive drum.

  According to the image processing apparatus of the present invention, an abnormal image caused by a drum scratch can be detected with high accuracy.

It is a figure which shows the structure of a system. It is a figure which shows the flow of an image process. It is a figure which shows the flow of an image diagnostic process. It is a figure which shows the flow of an image analysis process. It is a figure which shows the flow of a failure location diagnostic process. It is a figure which shows an example of the chart for a diagnosis. It is a figure which shows an example of UI which displays a failure location diagnostic result. It is a figure which shows an example of the scan image for a diagnosis. It is a figure which shows the outline | summary of the image analysis process at the time of white-out occurrence. It is a figure which shows the outline | summary of the image analysis process at the time of black solid generation | occurrence | production. It is a table | surface which classify | categorizes a drum flaw by correspondence with signal data and a correlation coefficient. It is a figure which shows the area division | segmentation which concerns on 2nd Embodiment. It is the figure which showed the example which extracts the maximum value which concerns on 3rd Embodiment.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a diagram illustrating a configuration of a system in the present embodiment.
The system in this embodiment includes an MFP 101 and a PC 124 as an example of an image processing apparatus. An MFP (Multi Function Printer) 101 that uses cyan, magenta, yellow, and black (hereinafter, C, M, Y, and K) toners is connected to other network-compatible devices via a network 123. The PC 124 is connected to the MFP 101 via the network 123. A printer driver 125 in the PC 124 transmits print data to the MFP 101.

  The MFP 101 will be described in detail. The network I / F 122 receives print data and the like. The controller 102 includes a CPU 103, a renderer 112, and an image processing unit 114. The interpreter 104 of the CPU 103 interprets the PDL (page description language) portion of the received print data and generates intermediate language data 105. The CMS 106 performs color conversion using the source profile 107 and the destination profile 108 to generate intermediate language data (after CMS) 111.

  Here, “CMS” is an abbreviation for “Color Management System”, and performs color conversion using information of a profile described later. The source profile 107 is a device-independent color space such as L * a * b * (hereinafter referred to as Lab) or XYZ in which a color space dependent on devices such as RGB and CMYK is defined by the CIE (International Lighting Commission). It is a profile for converting to. XYZ is a device-independent color space like Lab, and expresses colors with three types of stimulus values. The destination profile 108 is a profile for converting the device-independent color space into a CMYK color space depending on the device (printer 115).

  On the other hand, the CMS 109 performs color conversion using the device link profile 110 to generate intermediate language data (after CMS) 111. Here, the device link profile 110 is a profile for directly converting a device-dependent color space such as RGB or CMYK into a CMYK color space depending on the device (printer 115). Which CMS is selected depends on the setting in the printer driver 125.

  In this embodiment, the CMS (106 and 109) is divided according to the type of the profile (107, 108 and 110), but a plurality of types of profiles may be handled by one CMS. Further, the type of profile is not limited to the example given in the present embodiment, and any type of profile may be used as long as the device-dependent CMYK color space of the printer 115 is used.

  The renderer 112 generates a raster image 113 from the generated intermediate language data (after CMS) 111. The image processing unit 114 performs image processing on the raster image 113 and the image read by the scanner 119. Details of the image processing unit 114 will be described later. A printer 115 connected to the controller 102 is a printer that forms output data on paper using colored toners such as C, M, Y, and K. The printer 115 is controlled by the CPU 127 and includes a paper feeding unit 116 that feeds paper and a paper discharge unit 117 that discharges paper on which output data is formed.

  The display device 118 is a UI (user interface) that displays instructions to the user and the state of the MFP 101. In addition to copying, transmission processing, etc., it is used in image diagnostic processing described later. The scanner 119 is a scanner including an auto document feeder. The scanner 119 irradiates a bundle-like or single original image with a light source (not shown), and forms an original reflection image on a solid-state imaging device such as a CCD (Charge Coupled Device) sensor with a lens. Then, a raster-like image reading signal is obtained as image data from the solid-state imaging device.

  The input device 120 is an interface for receiving input from a user. Since some of the input devices are touch panels, they are integrated with the display device 118. The storage device 121 stores data processed by the controller 102, data received by the controller 102, and the like. The image diagnosis unit 126 performs an image diagnosis process for estimating a failure location (deficient location) by outputting a chart and executing an analysis process when an abnormal image occurs. Details of the image diagnosis processing will be described later.

FIG. 2 is a diagram illustrating a flow of image processing in the image processing unit 114.
The image processing unit 114 performs image processing on the raster image 113 and the image read by the scanner 119. The processing shown in FIG. 2 is realized by executing an ASIC (Application Specific Integrated Circuit) (not shown) in the image processing unit 114.

  In step S <b> 201, the image processing unit 114 determines whether the received image data is scan data read by the scanner 119 or a raster image 113 sent from the printer driver 125. If the received image is not scan data, since it is a raster image 113 that has been bitmap-developed by the renderer 112, the subsequent processing is performed as a CMYK image 210 converted into a CMYK color space depending on the printer device by the CMS.

  If the received image is scan data, since it is an RGB image 202, color conversion processing is performed in step S203 to generate a common RGB image 204. The common RGB image 204 is defined in a device-independent RGB color space, and can be converted into a device-independent color space such as Lab by calculation.

  On the other hand, in step S205, the image processing unit 114 performs character determination processing and generates character determination data 206. Here, the character determination data 206 is generated by detecting the edge of the image. Next, in step S207, the image processing unit 114 performs a filtering process on the common RGB image 204 using the character determination data 206. Here, the character determination data 206 is used to perform different filter processing for the character portion and other portions. In step S208, the image processing unit 114 performs background removal processing to remove ground color components.

  Next, in step S209, the image processing unit 114 performs color conversion processing to generate a CMYK image 210. In step S211, the image processing unit 114 corrects the gradation characteristics of each of C, M, Y, and K using the 1D-LUT. The 1D-LUT is a one-dimensional LUT (Look Up Table) that corrects each color of C, M, Y, and K. Finally, in step S212, the image processing unit 114 performs image formation processing such as screen processing and error diffusion processing to create a CMYK image (binary) 213.

<About diagnostic imaging processing>
Next, image diagnostic processing according to the present embodiment will be described with reference to FIG.
The image diagnosis process is a process executed by a user or the like when an abnormal image occurs, and is controlled by the image diagnosis unit 126.

  Among the processes shown in FIG. 3, the processes in steps S <b> 301 to S <b> 309 are realized by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. In the process of step S310, the display device 118 displays an instruction to the user on the UI, and the input device 120 receives the user's instruction. First, in step S301, the image diagnosis unit 126 acquires diagnostic chart data 302 for image quality diagnosis.

FIG. 6 is a diagram illustrating an example of diagnostic chart data.
The diagnostic chart data 601 is a chart for determining drum scratches, and is configured by a halftone patch 602 that is uniform in the surface in any one of C, M, Y, and K (color to be determined). In the present embodiment, a configuration using a single color has been described. However, the present invention is not limited to this. For example, an R patch configured by M and Y, or a G patch or a B patch may be used. Further, the diagnostic chart data 601 may be used not only to detect a drum scratch but also to detect an image abnormality caused by a cause different from the drum scratch.

  Returning to the description of FIG. In step S303, the image diagnosis unit 126 outputs the diagnostic chart data 302 acquired in step S301 with a printer, and acquires an output chart 304. In step S305, the image diagnosis unit 126 reads the output chart 304 with the scanner 119, and acquires a scan image for diagnosis. In step S306, the image diagnosis unit 126 performs image analysis processing for detecting drum scratches using the scanned image read in step S305, and calculates an image feature quantity 308. Details of the image analysis processing will be described later.

  In step S307, the image diagnosis unit 126 executes failure location diagnosis processing using the image feature amount 308 calculated in step S306, and outputs a failure location diagnosis result 309. Details of the failure location diagnosis processing will be described later. In step S310, the image diagnosis unit 126 displays the failure location diagnosis result in step S307 on the display device 118.

FIG. 7 is a diagram illustrating an example of a UI that displays a failure location diagnosis result according to the present embodiment.
A UI 701 shows an example of an abnormal image determination result. Here, specific words (messages) are displayed so as to be understood by the user, information encoded so that a service or the like can be determined quantitatively, and information indicating an exchange unit are displayed. Note that the displayed content and display method are not limited to this, and for example, an image indicating the content of a message, information indicating an exchange method, and the like may be displayed.

  If it is determined in step S307 that there is no faulty part (not an abnormal image), information indicating that there is no problem in the printer itself is displayed in step S310. In this way, since specific and quantitative information about the abnormal image is shown, the load corresponding to the abnormal image can be reduced and the response time can be shortened.

<About image analysis processing>
Next, details of the image analysis process in step S306 of the image diagnosis process (FIG. 3) will be described with reference to FIG.
The image analysis process uses the diagnostic scan image read in step S305 to analyze whether or not there is a periodic variation in the drum perimeter based on the characteristics of the abnormal image that appears when there is a drum scratch. It is processing to do. The image analysis process is controlled by the image diagnostic unit 126.

  Of the processes shown in FIG. 4, the processes in steps S <b> 401 to S <b> 407 are implemented by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. First, in step S401, the image diagnosis unit 126 acquires the scan image for diagnosis read in step S305.

FIG. 8 is a diagram illustrating an example of the read diagnostic scan image.
In this embodiment, as an example, a case where a drum scratch occurs on a K photosensitive drum will be described. That is, assume that the color to be determined is K, and the diagnostic chart data 601 in FIG. 6 outputs a K halftone patch 602. A diagnostic scan image 801 shown in FIG. 8 is image data of a K halftone patch 802 including an abnormal image due to a drum scratch on the K photosensitive drum.

  The halftone patch 802 includes the following areas as an abnormal image due to drum scratches. In other words, the drum scratched areas 803 and 804 that have fallen white when the surface of the photoconductive drum is shallowly scratched (black solid) are printed. And drum scratch generation areas 805 and 806. The halftone patch 802 includes dirty areas 807 and 808 as dirty abnormal images not caused by drum scratches.

  The peripheral length of the photosensitive drum in this embodiment is 94.2 mm. A diagnostic scan image 801 shown in FIG. 8 shows a case where printing is performed with 210 mm, which is A4 size paper and the conveyance direction is short. In this case, two or three abnormal images are generated by one drum scratch. In step S402, the image diagnosis unit 126 performs region division of the diagnostic scan image acquired in step S401. A region dividing method according to the present embodiment will be described with reference to FIGS.

FIG. 9 is a diagram showing an outline of image analysis processing when white spots occur due to drum scratches.
The image data 901 is image data obtained by scanning an image including white drum damage generation areas 911 and 912 and black solid drum damage generation areas 912 and 913.

FIG. 10 is a diagram showing an outline of image analysis processing when black solids due to drum scratches occur.
The image data 1001 is image data obtained by scanning an image including white drum damage generation areas 1011 and 1012 and black solid drum damage generation areas 1013 and 1014.

  The area division of the scanned image is performed in the transport direction as shown in FIGS. That is, the vertical direction is divided. When the periodicity is analyzed with a small number of data of the drum scratch perimeter, when an abnormal image unrelated to the drum scratch such as the dirt areas 807 and 808 in FIG. 8 is generated, Different data increase, and the periodicity determination accuracy decreases. Therefore, it is possible to improve the accuracy of analyzing the periodicity by performing region division in the transport direction and facilitating extraction of regions where abnormal images due to drum scratches are generated.

  In this embodiment, the image data 1001 is divided into 10 parts and analyzed in order to divide the white drum scratched area having a width of 20 mm into the same area. As shown in FIG. 9 and FIG. 10, by dividing the area, the divided area (divided area) A can be assigned to a blank drum scratch generation area. Further, the divided area B can be assigned to a black solid drum scratch generation area.

  Returning to the description of FIG. In step S403, the image diagnosis unit 126 calculates the maximum value and the minimum value of luminance for each row of pixels (pixel columns) positioned in the direction perpendicular to the transport direction for each region divided in step S402. To do. Then, signal data (first data) in which the calculated maximum value signal data and minimum value signal data are arranged in the transport direction is generated. By calculating the maximum luminance signal data, it is possible to analyze the periodicity of white spots due to drum scratches, and by calculating the minimum luminance signal data, it is possible to analyze the periodicity of black solids due to drum scratches. In the calculation of the minimum luminance value, the minimum luminance value of the color of the halftone patch being analyzed is extracted.

  As a result, in the divided area A in FIG. 9, the maximum value signal data 903 becomes signal data in which the maximum value is changed so as to correspond to the blank drum scratch generation area. Further, the minimum value signal data 904 is signal data that is not related to white spots due to drum scratches. Similarly, in the divided region B in FIG. 10, the maximum value signal data 1003 is signal data that is not related to the solid black due to drum scratches. Further, the minimum value signal data 1004 is signal data in which the minimum value is changed so as to correspond to the black solid drum scratch generation region.

  In step S404, the image diagnosis unit 126 performs difference extraction on the maximum value signal data and the minimum value signal data calculated in step S403. Specifically, for the maximum value signal data and the minimum value signal data, the difference between the data point (pixel) one pixel before the data point (pixel) of interest in the transport direction is calculated, Difference signal data (second data) is generated. As a result, in the area A of FIG. 9, the maximum difference signal data 905 becomes the difference signal data in which the difference between the pixels corresponding to the blank drum scratch generation area increases. Further, the difference signal data 906 having the minimum value is difference signal data having a smaller difference compared to the difference signal data having the maximum value.

  Similarly, in the region B of FIG. 10, the difference signal data 1006 having the minimum value is difference signal data in which the difference between pixels corresponding to the black solid drum flaw occurrence region is increased. Further, in region B of FIG. 10, the maximum difference signal data 1005 is difference signal data having a smaller difference than the minimum difference signal data. In the present embodiment, the difference signal data is calculated as the difference signal data from the signal data of the previous pixel in the transport direction. However, the present invention is not limited to this, and it is only necessary to extract a point having a large difference from the surrounding pixels. . For example, a difference from a predetermined average value of signal data of a plurality of previous pixels may be calculated.

  In step S405, the image diagnostic unit 126 performs threshold processing on the maximum difference signal data and the minimum difference signal data calculated in step S404, and extracts extracted signal data (third data extracted only with large difference amounts). Data). Specifically, at each point of the differential signal data with the maximum value and the differential signal data with the minimum value, a point having a value smaller than the threshold value is set to 0, and only values of points that are equal to or greater than the threshold value are extracted. That is, by extracting only values having a large value (that is, a difference) at each point from the differential signal data having the maximum value and the differential signal data having the minimum value, and performing the process of making the waveform of the differential signal data stand out, the periodicity is improved. When detecting, it is possible to eliminate the influence of density unevenness.

  Note that the thresholds used for the maximum difference signal data and the minimum difference signal data are respectively predetermined values. In this embodiment, the threshold value of the maximum difference signal data is set to a value that is 50% of the difference between the signal data when there is no abnormal image and the signal data of paper on which no toner is placed. The threshold value of the minimum difference signal data is set to a value that is 50% of the difference between the signal data when there is no abnormal image and the solid signal data.

  As a result, in the area A of FIG. 9, the maximum value extracted signal data 907 is extracted only in the difference signal data in which the difference exceeds the threshold in the white drum scratch occurrence area, and the minimum value extracted signal data 908 is all Since it is below the threshold value, it becomes 0 signal data at all points. Similarly, in the region B of FIG. 10, all of the extracted signal data 1007 with the maximum value is equal to or less than the threshold value. Therefore, the extracted signal data 1008 with the minimum value becomes zero signal data at all points. Only differential signal data whose difference exceeds a threshold value in the scratch occurrence area is extracted.

Next, in step S406, the image diagnosis unit 126 performs autocorrelation analysis on the maximum value extracted signal data and the minimum value extracted signal data subjected to the threshold processing in step S405, and calculates autocorrelation coefficient data. The autocorrelation coefficient data is calculated using the autocorrelation function _Rx shown below.

At this time, x is extracted signal data, N is the number of pixels in the carrying direction, and τ is the shift amount.
The autocorrelation coefficient data φ _x is calculated as follows using the calculated autocorrelation function R _x .

In the autocorrelation coefficient data, τ having a large correlation coefficient indicates a periodic interval. If the correlation coefficient τ increases with the drum perimeter, it can be determined that there is a drum flaw. When R _x (0) is 0, the autocorrelation coefficient data φ _x is 0 because the autocorrelation coefficient data cannot be calculated.

As a result, in region A in FIG. 9, the autocorrelation coefficient data 909 relating to the maximum value of the extracted signal data has a high correlation coefficient with the peripheral length of the drum. In the region A of FIG. 9, the autocorrelation coefficient data 910 relating to the extracted signal data of the minimum value has a low correlation coefficient at the circumference of the drum because R _x (0) is 0. Similarly, in region B of FIG. 10, the autocorrelation coefficient data 1010 related to the minimum value extracted signal data has a high correlation coefficient at the circumference of the drum, and the autocorrelation coefficient data 1009 related to the maximum value extracted signal data. R _x (0) becomes 0. For this reason, the correlation coefficient at the circumference of the drum is low.

  Next, in step S407, the image diagnosis unit 126 determines whether the correlation coefficient of the pixel corresponding to the known perimeter of the drum for each divided area exceeds the threshold in the calculated autocorrelation coefficient data. The determination result is stored in the storage device 121 as the image feature amount 308. Note that the threshold used for determination is a predetermined value. The threshold in the present embodiment is 0.3, which can be determined to be statistically correlated, but is not limited to this, and may be set arbitrarily by the user, for example.

FIG. 11 is a table in which drum scratches are classified according to correspondence between signal data that is characteristic of drum scratches and correlation coefficients.
Specifically, in the change in the signal data of the maximum value or the minimum value of luminance, the table classifies the presence and type of drum scratches according to whether or not the autocorrelation coefficient in the drum perimeter exceeds a threshold value. . If the autocorrelation coefficient does not exceed the threshold value in any of the minimum value and maximum value signal data, it is determined that no drum scratch has occurred. When the autocorrelation coefficient exceeds the threshold value in the change in the maximum value of the signal data, a drum flaw has occurred, and it is determined that the flaw is shallow with respect to the center direction of the drum.

  In addition, when the autocorrelation coefficient exceeds the threshold in the change in the minimum signal data, a drum scratch has occurred, and it is determined that the scratch has a deep scratch depth with respect to the center direction of the drum. To do. In the present embodiment, 0 is stored as the image feature quantity 308 when drum scratches have not occurred in all the divided areas. If a drum flaw has occurred in any region, information describing the determined color and the determined maximum or minimum correlation coefficient is stored as a feature amount. If the correlation coefficient is high for both the minimum value and the maximum value for the same color, both pieces of information are stored as the image feature quantity 308.

<About fault location diagnosis processing>
Next, details of the failure location diagnosis process in step S307 of the image diagnosis process (FIG. 3) will be described with reference to FIG.
The fault location diagnosis process is a process for diagnosing a specific phenomenon and a faulty part using the image feature quantity 308 calculated in step S306. The fault diagnosis process is controlled by the image diagnosis unit 126.

  Of the processes shown in FIG. 5, the processes in steps S <b> 501 to S <b> 506 are implemented by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. First, in step S501, the image diagnosis unit 126 acquires the image feature amount 308 relating to the determined color calculated in step S306. In step S502, the image diagnosis unit 126 determines whether or not there is a periodicity of the peripheral length of the drum in the change in the signal data of the minimum luminance value from the information included in the image feature amount 308 acquired in step S501. judge.

  If it is determined that there is periodicity of the drum circumference, the process proceeds to step S503. In step S503, the image diagnosis unit 126 determines that there is a deep drum scratch on the photosensitive drum of the determined color. In step S506, the determination result is output. In addition, after the process of step S503, the process may proceed to step S504 to determine whether there is a shallow drum scratch. If it is determined that there is no periodicity of the drum circumference, the process proceeds to step S504.

  In step S504, the image diagnosis unit 126 determines whether or not there is a periodicity of the drum circumference in the change in the signal data of the maximum luminance value from the information included in the image feature quantity 308 acquired in step S501. judge. If it is determined that there is periodicity of the drum circumference, the process proceeds to step S505. In step S505, the image diagnosis unit 126 determines that there is a shallow drum scratch on the photosensitive drum of the determined color. In step S506, the determination result is output.

  In step S506, the result determined so far is output as a failure location diagnosis result 309. If it is determined from the information included in the image feature quantity 308 that no drum scratch has occurred, the fact that there is no drum scratch on the photosensitive drum associated with the determined color is output as a failure location diagnosis result.

  In this embodiment, in order to diagnose drum scratches, signal data that is characteristic of drum scratches is extracted, and it is determined whether or not the extracted signal data has a periodicity of the peripheral length of the photosensitive drum. It was. The signal data that is characteristic of drum scratches was determined as follows. The scanned image was divided into regions in the transport direction, and the maximum value and the minimum value of the luminance of pixels located in the direction perpendicular to the transport direction were calculated. Then, the difference from the previous pixel is calculated for each of the maximum value and the minimum value, and a signal whose difference amount exceeds the threshold value is a signal having the characteristic of drum scratches.

  Further, whether or not there is a periodicity of the peripheral length of the photosensitive drum was determined as follows. Auto-correlation analysis is performed on the extracted signal data that characterizes the drum scratches, and it is determined whether the correlation coefficient is high due to the periodicity of the drum circumference. It was determined that there was a drum scratch. Note that the present invention is not limited to the method described in this embodiment, and any method can be used as long as it can extract signal data that is characteristic of drum scratches and determine the periodicity of the peripheral length of the photosensitive drum. Good.

  As described above, according to the present embodiment, an abnormal image caused by a drum scratch can be identified with high accuracy without being affected by a phenomenon having periodicity like a drum scratch such as density unevenness, for example. can do.

(Second Embodiment)
In the first embodiment, in order to diagnose drum scratches, signal data that is characteristic of drum scratches is extracted, and it is determined whether or not the extracted signal data has a periodicity of the circumference of the photosensitive drum. went. On the other hand, in this embodiment, a method different from that of the first embodiment is used for area division.

  For example, when a scan image for diagnosis is read and the document is tilted, the region dividing method according to the first embodiment has a region where an abnormal image (solid black or white) is generated in different divided regions. It is possible to do. When a periodic abnormal image exists in a different area, the periodicity cannot be analyzed, and the analysis accuracy is lowered.

  Further, as a factor other than the inclination of the document, for example, periodic abnormal images may exist in different regions due to the influence of reading variations during scanning. In this embodiment, in this way, it is possible to analyze the drum scratches without being affected by the inclination of the scanned document and the reading variation during scanning. In addition, since the image diagnosis process and the fault location diagnosis process in this embodiment are the same as those in the first embodiment, the description thereof is omitted. In the present embodiment, only differences from the first embodiment will be described.

The image analysis processing in this embodiment will be described with reference to FIG.
The image analysis processing in this embodiment is controlled by the image diagnostic unit 126. The processing in steps S <b> 401 to S <b> 407 is realized by execution by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. In step S402, the image diagnosis unit 126 performs region division of the diagnostic scan image acquired in step S401. A region dividing method according to the present embodiment will be described with reference to FIG.

FIG. 12A is a diagram illustrating a case where an abnormal image due to a drum scratch occurs on the boundary between regions when the region is divided as in the first embodiment.
In FIG. 12A, black solid areas 1201 and 1202 due to drum scratches are generated in different areas due to the influence of the inclination of the scanned document. When the periodicity is analyzed for each divided area, the periodicity of the black solid areas 1201 and 1202 cannot be determined.

  On the other hand, in this embodiment, in the adjacent areas as shown in FIG. 12B, the areas are divided so as to include (overlapping) predetermined areas. In the area A of FIG. 12B, there are black solid drum flaw occurrence areas 1203 and 1204 that are abnormal images due to the same drum flaw, and in the area B, only the black solid drum flaw occurrence area 1204 exists. Exists. Thus, by performing image analysis processing on the area A in the same manner as in the first embodiment, it is possible to confirm the periodicity of the peripheral length of the photosensitive drum due to the black solid drum damage generation areas 1203 and 1204. it can.

  As described above, in the present embodiment, the image analysis processing is performed by dividing the regions so that the predetermined regions are included in the adjacent regions. As a result, it is possible to analyze by including periodic abnormal images due to drum scratches in the same area without being affected by the inclination of the scanned document and the influence of reading variations during scanning. Note that the method is not limited to the method described above, and any method that can analyze a periodic abnormal image due to a drum scratch in the same area without being affected by the inclination of the scanned document or the influence of reading variations during scanning. Any method is acceptable.

(Third embodiment)
In the second embodiment, the method of analyzing the drum scratches from the abnormal image without being affected by the inclination of the scanned document and the influence of reading variations during scanning has been described. In contrast, in the present embodiment, a method for analyzing a drum scratch from an abnormal image without reducing the analysis accuracy while reducing the amount of calculation will be described.

  In addition, since the image diagnosis process and the fault location diagnosis process in this embodiment are the same as those in the first embodiment, the description thereof is omitted. In the present embodiment, only differences from the first embodiment and the second embodiment will be described.

The image analysis processing in this embodiment will be described with reference to FIG.
The image analysis processing in this embodiment is controlled by the image diagnostic unit 126. The processing in steps S <b> 401 to S <b> 407 is realized by execution by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121.

  In step S403, the image diagnostic unit 126 calculates the maximum value and the minimum value of the brightness in the direction perpendicular to the transport direction and for each of a plurality of predetermined rows for each region divided in step S402. Then, the maximum value signal data and the minimum value signal data which are time-series data in the transport direction are calculated. A method for calculating the signal data of the maximum value and the minimum value in the present embodiment will be described with reference to FIG.

  FIG. 13A is a diagram illustrating the maximum value signal data of the region A calculated by the method described in the first embodiment. A maximum value is calculated for each pixel positioned in a direction perpendicular to the transport direction, and data obtained by arranging the calculated maximum values in the transport direction is used as one signal data (maximum value signal data). On the other hand, FIG. 13B is a diagram showing the maximum value signal data of the region A calculated by the method described in the present embodiment. The maximum value for every two rows of pixels positioned in the direction perpendicular to the transport direction is calculated, and data obtained by arranging the calculated maximum values in the transport direction is used as one signal data (maximum value signal data).

  As a result, the number of data points included in one signal data is reduced, and the overall calculation amount can be reduced. In the present embodiment, one data point is calculated for every two rows. However, the present invention is not limited to this, and the peak value of the other image forming member and the peak value of the peripheral length of the drum are the same. If not, one data point may be used for every three or more rows.

  As described above, in this embodiment, when calculating the signal data in which the maximum value and the minimum value of luminance are arranged, one data point is generated for each of a plurality of rows of pixel columns positioned in the direction perpendicular to the transport direction. As a result, the amount of calculation can be reduced. Note that the present invention is not limited to this embodiment, and any method may be used as long as the method can reduce the amount of calculation and analyze the drum scratches.

(Other embodiments)
The present invention supplies a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and one or more processors in a computer of the system or apparatus read and execute the program This process can be realized. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

  As mentioned above, although preferable embodiment of this invention was described, this invention is not limited to these embodiment, A various deformation | transformation and change are possible within the range of the summary.

101 MFP
103 CPU
126 diagnostic imaging department

Claims (11)

A dividing unit that divides the image into a plurality of regions in the conveyance direction of the recording material;
Generating means for obtaining a maximum value or a minimum value of luminance of pixels located in a direction perpendicular to the transport direction for each region, and generating first data in which the maximum value or the minimum value is arranged in the transport direction;
Based on the first data, when a point having a large difference from a peripheral pixel in at least one of the areas is repeatedly detected at a period of the peripheral length of the photosensitive drum, it is determined that a drum scratch is present And an image processing apparatus. A difference extracting unit that generates second data obtained by calculating a difference between each point of the first data and the previous point with respect to the first data;
Threshold value processing means for generating third data with the value of 0 when the value of each point of the second data is equal to or greater than a predetermined threshold value,
The determination means determines whether or not a point having a large difference from the surrounding pixels is repeatedly detected with a period of a peripheral length of the photosensitive drum, using the third data. The image processing apparatus according to claim 1. The determination unit determines whether or not a point having a large difference from surrounding pixels is repeatedly detected in a period of the peripheral length of the photosensitive drum by performing autocorrelation analysis on the third data. To
The image processing apparatus according to claim 2. 4. The division unit according to claim 1, wherein, when dividing the image into a plurality of areas in the transport direction, each of the areas is divided so as to overlap with an adjacent area with a predetermined width. The image processing apparatus according to item 1. The generating unit obtains a maximum value or a minimum value among luminances of pixels included in a pixel row positioned in a direction perpendicular to the transport direction for each region, and arranges the maximum value or the minimum value in the transport direction and sets 5. The image processing apparatus according to claim 1, wherein the data of 1 is generated. The generation unit calculates a maximum value or a minimum value among luminances of pixels included in the plurality of pixel columns, and generates the first data by arranging the maximum value or the minimum value in a transport direction. Item 6. The image processing apparatus according to Item 5. The determination unit repeatedly detects the point where the first data is generated using the maximum value of the luminance and the difference from the peripheral pixels is large at a period of the peripheral length of the photosensitive drum. The image processing apparatus according to claim 1, wherein when it is determined, there is a drum scratch having a shallow scratch depth in the center direction of the drum. The determination unit repeatedly detects the point where the difference between the first data is generated using the minimum value of the luminance and the peripheral pixels is a cycle of the peripheral length of the photosensitive drum. The image processing apparatus according to claim 1, wherein when it is determined, there is a drum scratch having a deep scratch in the center direction of the drum. A display unit for displaying a determination result of the determination unit;
Based on the determination result, the display means displays at least one of a message indicating whether or not the cause of the abnormal image is due to drum scratches, coded information indicating the determination result, and information indicating the replacement unit. The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. A division step of dividing the image into a plurality of regions in the conveyance direction of the recording material;
For each region, a generation step of obtaining a maximum value or a minimum value of luminance of pixels located in a direction perpendicular to the transport direction and generating first data in which the maximum value or the minimum value is arranged in the transport direction;
Based on the first data, when a point having a large difference from a peripheral pixel in at least one of the areas is repeatedly detected at a period of the peripheral length of the photosensitive drum, it is determined that a drum scratch is present A control method for an image processing apparatus.   The program for functioning a computer as each means with which the image processing apparatus of any one of Claim 1 thru | or 9 is provided.