Inspection device, its control method, and program

Abstract

To solve the problem that RIP data (reference image) used for printing and the scan image (inspection target image) obtained by scanning printed matter give rise to a difference in edge reproducibility, and this results in a difference between the scan image and the RIP data becoming increased, causing over-detections to increase.SOLUTION: Provided is an article inspection device for detecting a difference between image data acquired by reading sheets and a reference image of the image data, with edge corrections performed using an edge correction parameter on the lines that are included in second image data acquired from the print data of a patch image. An edge correction parameter with which a difference between the concentration of this edge-corrected second image data and the concentration of first image data acquired by reading a sheet to which the patch image is printed becomes minimal, is acquired and stored in memory.SELECTED DRAWING: Figure 9

Description

Technical Field

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[0001] The present invention relates to an inspection device, a control method thereof, and a program.

Background Art

[0002] In a printing and binding system such as a POD (Print On Demand) machine, there is an inspection system that recognizes (inspects) the image quality formed on the output paper (printed matter) after printing and detects image defects. The inspection process in this inspection system aligns the RIP (Raster Image Processing) data (reference image) obtained by expanding the data of the page description language (PDL) used for printing with the scanned image (image data of the inspection target) obtained by scanning the printed matter. Then, the image quality of the printed matter is determined by performing image comparison and determination processing, and image defects are detected.

[0003] Patent Document 1 describes a system for inspecting the image quality of a printed matter using RIP data (reference image) and a scanned image (image data of the inspection target).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] <0In the inspection process described above, a difference arises in edge reproducibility between the RIP data (reference image) used for printing and the scanned image (inspection target image) obtained by scanning the printed material. For example, due to dot gain and reading effects, the scanned image may appear thicker, mainly at the edges and on thin lines, compared to the RIP data. This leads to a larger difference between the scanned image and the RIP data, resulting in an increase in false positives. Therefore, if the detection rate is reduced to prevent such false positives, it becomes impossible to detect image defects that should be detected.

[0006] The object of the present invention is to solve at least one of the problems of the prior art described above.

[0007] The objective of this invention is to solve the above problems by matching the edge reproducibility of the reference image and the image to be inspected. [Means for solving the problem]

[0008] To achieve the above objective, an inspection apparatus according to one aspect of the present invention has the following configuration. That is, An inspection device that detects the difference between image data obtained by reading a sheet and a reference image of said image data, A reading means that reads a sheet on which a patch image is printed and obtains first image data, A first acquisition means for acquiring a second image data from the print data of the aforementioned patch image, A correction means that performs edge correction by applying edge correction parameters to lines included in the second image data, A second acquisition means for acquiring edge correction parameters that minimize the difference between the density of the second image data, which has been edge-corrected by the correction means, and the density of the first image data. A storage means for storing edge correction parameters acquired by the second acquisition means, death, The aforementioned patch image includes thin lines, The edge correction parameters include settings related to the process of thickening the thin lines and the process of smoothing them. The density of the second image data and the density of the first image data are the average density of the reference region containing a plurality of the thin lines in the patch image.It is characterized by the following: [Effects of the Invention]

[0009] According to the present invention, there is an effect of reducing false positives by matching the edge reproducibility of the reference image and the image to be inspected.

[0010] Other features and advantages of the present invention will become apparent from the following description with reference to the accompanying drawings. In the accompanying drawings, the same or similar components are given the same reference numeral. [Brief explanation of the drawing]

[0011] The attached drawings are included in the specification and constitute part thereof, illustrating embodiments of the present invention and are used together with the description to explain the principles of the present invention. [Figure 1] A diagram showing an example of a system configuration including an inspection device according to Embodiment 1 of the present invention. [Figure 2] A block diagram illustrating the hardware configuration of the image forming apparatus according to Embodiment 1. [Figure 3] A diagram illustrating the mechanism of the printer section of the image forming apparatus according to Embodiment 1. [Figure 4] Figure (A) shows a schematic diagram of the internal configuration of the inspection device according to Embodiment 1, and Figure (B) shows a top view of the conveyor belt as seen from the inspection sensor side. [Figure 5] A block diagram illustrating the configuration of the inspection device control unit of the inspection device according to Embodiment 1. [Figure 6] A flowchart illustrating the inspection process using the inspection device according to Embodiment 1. [Figure 7] A flowchart illustrating the process by which the inspection device according to Embodiment 1 generates calibration data and prints a calibration chart. [Figure 8] A flowchart illustrating the process by which the inspection device according to Embodiment 1 reads a calibration chart and obtains patch density from a scanned image. [Figure 9]Flowchart for explaining the process of determining edge correction parameters by the inspection device according to Embodiment 1. [Figure 10] Diagram showing a patch included in the calibration chart according to Embodiment 1 and an example of the calibration chart. [Figure 11] Enlarged schematic diagram (A) of the reference area of the patch image obtained in S802 of FIG. 8, enlarged schematic diagram (B) of the reference area of the patch image obtained from the RIP data, diagrams (C)(D) showing the result of gradually thickening the patch image of FIG. 11(B) using edge correction parameter 2, and diagrams (E)-(G) showing the result of performing smoothing processing on the patch images of FIGS. 11(B) to 11(D). [Figure 12] Diagram (A) showing the determination criteria for whether there is an image defect when the image feature amount is the area and the average difference value, and diagram (B) showing an example of the menu screen for instructing calibration setting, calibration execution, and inspection execution displayed on the operation unit / display unit of the inspection device according to the embodiment. [Figure 13] Diagram (A) showing an example of the calibration setting screen according to Embodiment 1 and diagram (B) showing an example of the calibration registration screen. [Figure 14] Enlarged schematic diagrams (A)(B) of the reference area of the patch image obtained by scanning in S802 of Embodiment 1 and diagram (C) showing the conversion formula of the affine transformation. [Figure 15] Explanatory diagram (A) of the filter in Embodiment 2, diagram (B) showing the relationship between the edge correction parameter according to Embodiment 1, the thickening process, and the smoothing process, and diagram (C) showing an example of storing the edge correction parameter according to Embodiment 1.

Mode for Carrying Out the Invention

[0012] Embodiments of the present invention will be described in detail below with reference to the attached drawings. Note that the following embodiments do not limit the invention to the claims. While multiple features are described in the embodiments, not all of these features are essential to the invention, and the features may be combined in any way. Furthermore, in the attached drawings, the same or similar configurations are given the same reference numerals, and redundant descriptions are omitted.

[0013] [Embodiment 1] Figure 1 shows an example of a system configuration including an inspection device according to Embodiment 1 of the present invention.

[0014] The image forming apparatus 100 processes various input data and produces print output. The inspection device 200 receives the printed material output by the image forming apparatus 100 and inspects the contents of the printed material. The finisher 300 receives the output paper (printed material) inspected by the inspection device 200 and performs post-processing such as binding. The image forming apparatus 100 is connected to an external print server and client PC via a network. The inspection device 200 is also connected to the image forming apparatus 100 on a one-to-one basis via a communication cable. The finisher 300 is also connected to the image forming apparatus 100 on a one-to-one basis via a different communication cable. The inspection device 200 and the finisher 300 are also interconnected via separate communication cables. Embodiment 1 shows an inline inspection system that performs image forming, image inspection, and finishing in an integrated manner.

[0015] Figure 2 is a block diagram illustrating the hardware configuration of the image forming apparatus 100 according to Embodiment 1.

[0016] This image forming apparatus 100 is an example of the image forming apparatus of the present invention and comprises a controller 21, a printer unit 206, and a UI unit (operation unit) 23. The UI unit 23 includes various switches and displays for operation.

[0017] Image data and document data created by software applications such as printer drivers (not shown) on client PCs on the network or print servers are transmitted as PDL data to the image forming apparatus 100 via the network (e.g., Local Area Network). In the image forming apparatus 100, the controller 21 receives the transmitted PDL data. The controller 21 is connected to the printer unit 206, and upon receiving the PDL data from the client PC or print server, it converts the PDL data into print data that can be processed by the printer unit 206, and outputs the print data to the printer unit 206.

[0018] The printer unit 206 prints the image based on the print data output from the controller 21. The printer unit 206 in Embodiment 1 uses an electrophotographic printing engine. However, the printing method is not limited to this; for example, an inkjet (IJ) method may also be used.

[0019] The UI unit 23 is operated by the user and is used to select various functions and give operation instructions. This UI unit 23 includes a display unit with a touch panel on its surface, and a keyboard with various keys such as a start key, stop key, and numeric keypad.

[0020] Next, the details of the controller 21 will be described. The controller 21 includes a network interface unit 101, a CPU 102, RAM 103, ROM 104, an image processing unit 105, an engine interface unit 106, and an internal bus 107. The network interface unit 101 is an interface for receiving PDL data transmitted from a client PC or print server. The CPU 102 controls the entire image forming apparatus 100 using programs and data stored in the RAM 103 and ROM 104, and also executes the processing performed by the controller 21, as described later. The RAM 103 provides a work area used by the CPU 102 when executing various processes. The ROM 104 stores programs and data for the CPU 102 to execute the various processes described later, as well as setting data for the controller 21.

[0021] The image processing unit 105 performs print image processing on the PDL data received by the network I / F unit 101 according to the settings from the CPU 102, and generates print data that can be processed by the printer unit 206. In particular, the image processing unit 105 generates image data (RIP data) with multiple color components per pixel by rasterizing the received PDL data. Multiple color components refer to independent color components in a color space such as RGB (red, green, blue). The image data has, for example, 8 bits (256 gradations) of value for each color component for each pixel. That is, the image data is multi-level bitmap data that includes multi-level pixels. In addition to the image data, the above rasterization also generates attribute data that shows the attributes of each pixel in the image data. This attribute data indicates what type of object the pixel belongs to, and is a value that indicates the type of object, such as text, line, graphic, image, or background. The image processing unit 105 generates print data by performing image processing such as color conversion from the RGB color space to the CMYK (cyan, magenta, yellow, black) color space and screen processing using the generated image data and attribute data.

[0022] The engine I / F unit 106 is an interface that transmits print data generated by the image processing unit 105 to the printer unit 206. The internal bus 107 is a system bus that connects the above-mentioned units and transmits control signals and the like.

[0023] Figure 3 is a diagram illustrating the mechanism of the printer unit 206 of the image forming apparatus 100 according to Embodiment 1.

[0024] The image forming apparatus 100 includes a scanner unit 301, a laser exposure unit 302, a photosensitive drum 303, an image forming unit 304, a fuser unit 305, a paper feeding / transport unit 306, and a printer control unit 308 that controls these units. The scanner unit 301 illuminates a document placed on the document table to optically read the document image, converts the image into an electrical signal, and creates image data. The laser exposure unit 302 directs a light beam, such as laser light modulated according to the image data, into a rotating polyhedron mirror (polygon mirror) 307 that rotates at a constant angular velocity, and irradiates the photosensitive drum 303 with reflected scanning light. The image forming unit 304 rotates the photosensitive drum 303, charges it with a charger, and develops the latent image formed on the photosensitive drum by the laser exposure unit 302 with toner. The toner image is then transferred to paper, and any minute toner remaining on the photosensitive drum that is not transferred is collected. Image formation is achieved by having four development units (developing stations) that perform this series of electrophotographic processes.

[0025] The four developing units, arranged in the order of cyan (C), magenta (M), yellow (Y), and black (K), sequentially perform the magenta, yellow, and black imaging operations after a predetermined time has elapsed since the start of imaging at the cyan station.

[0026] The fixing unit 305 is composed of a combination of rollers and belts, and incorporates a heat source such as a halogen heater. It melts and fixes the toner on the paper onto which the toner image has been transferred by the image forming unit 304 using heat and pressure. When printing on thick paper, the paper is thicker and has poor thermal conductivity, so the speed at which the paper passes through the fixing unit 305 must be reduced to, for example, half the normal speed. Consequently, when printing on thick paper, the paper transport speed of each part other than the fixing unit 305 is also halved, resulting in the printing speed of the image forming apparatus 100 being halved.

[0027] The paper feeding / transportation unit 306 has one or more paper storage compartments, such as paper cassettes or paper decks, and separates one sheet of paper from among the multiple sheets stored in the paper storage compartment according to the instructions of the printer control unit 308 and transports it to the image formation unit 304. The toner images of each color are transferred to the transported paper by the aforementioned developing station, and finally a full-color toner image is formed on the paper. Furthermore, when forming an image on both sides of the paper, the transport path is controlled so that the paper that has passed through the fixing unit 305 is transported again to the image formation unit 304.

[0028] The printer control unit 308 communicates with the controller 21, which controls the entire image forming apparatus 100, and executes control according to its instructions. The printer control unit 308 also manages the status of each of the aforementioned parts, such as the scanner, laser exposure, image formation, fixing, and paper feeding / transport, and issues instructions to ensure that the entire system operates smoothly and in harmony.

[0029] Figure 4(A) is a diagram illustrating the schematic internal configuration of the inspection device 200 according to Embodiment 1.

[0030] The paper (printed material) output from the image forming apparatus 100 is drawn into the inspection apparatus 200 by the paper feed roller 401. The printed material is then transported by the conveyor belt 402 and read by the inspection sensor 403 located on the conveyor belt 402. The inspection apparatus control unit 405 uses the image data (scanned image) obtained from the inspection sensor 403 to perform the inspection process. The inspection apparatus control unit 405 also controls the entire inspection apparatus 200. The inspection results are then sent to the finisher 300. After the inspection, the printed material is output from the paper discharge roller 404. Although not shown here, the inspection sensor 403 may also be configured to read from the underside of the conveyor belt 402 to accommodate double-sided printed materials.

[0031] Figure 4(B) is a top view of the conveyor belt 402 as seen from the inspection sensor 403 side.

[0032] Here, the inspection sensor 403 is a line sensor that reads an image of the entire surface of the conveyed printed material 410 line by line, as shown in the figure. The illumination device 411 illuminates the printed material when it is read by the inspection sensor 403. The skew detection illumination device 412 is a device for detecting whether the printed material 410 is skewed relative to the paper conveying direction as it is conveyed on the conveyor belt 402. The skew detection illumination device 412 illuminates the conveyed printed material 410 from an oblique direction, so that the inspection sensor 403 reads the shadow image of the edge of the printed material 410 and detects the skew of the printed material 410. In Embodiment 1, the reading of the shadow image of the edge of the printed material 410 is performed by the inspection sensor 403, but it is also possible to use a different reading sensor other than the inspection sensor 403.

[0033] Figure 5 is a block diagram illustrating the configuration of the inspection device control unit 405 of the inspection device 200 according to Embodiment 1.

[0034] The control of the inspection device control unit 405 is entirely performed by the control unit 503. The control unit 503 has a CPU 515, which executes a program loaded into the memory unit 504 to perform various processes described later. The image input unit 501 receives a scanned image obtained by reading a printed document with the inspection sensor 403. The CPU 515 stores this received scanned image in the memory unit 504. The communication unit 502 communicates with the controller 21 of the image forming apparatus 100. This communication involves receiving image data (reference image) used for printing, corresponding to the scanned image, and sending and receiving inspection control information. The CPU 515 also stores the received reference image and inspection control information in the memory unit 504.

[0035] One type of inspection control information exchanged with the image forming apparatus 100 is synchronization information for matching scanned images (inspection images) with reference images, such as print job information, print quantity information, and page order information. Another type of information is inspection result information and control information that controls the operation of the image forming apparatus 100 accordingly. The synchronization information is necessary to synchronize the reference image and scanned image in cases where the order in which the scanned image and the reference image used to print it are received by the inspection control device 102 differs, such as in double-sided printing or printing multiple copies. Furthermore, the synchronization information is necessary to synchronize the reference image and scanned images in cases where one reference image corresponds to multiple scanned images. The inspection control information exchanged with the finisher 300 is inspection result information and control information that controls the operation of the finisher 300 accordingly.

[0036] The inspection processing unit 513 is controlled by the CPU 515 of the control unit 503. Based on the synchronization information, which is one of the inspection control information exchanged with the image forming apparatus 100 as described above, the inspection processing unit 513 sequentially performs inspection processing on corresponding scan image and reference image pairs using the inspection processing unit 513. Details of the inspection processing unit 513 will be described later. When the inspection processing is completed, the judgment result is sent to the control unit 503 and displayed on the operation unit / display unit 505. If an image defect is found as a result of this judgment, the operation unit / display unit 505 switches the control of the image forming apparatus 100 and the finisher 300 via the communication unit 502 in a manner predetermined by the user. For example, it stops the image forming process by the image forming apparatus 100 and switches the output tray of the finisher 300 to the escape tray.

[0037] Next, I will explain the configuration of the inspection processing unit 513.

[0038] The skew detection unit 506 is a module that detects the skew angle of the scanned image. As mentioned above with reference to Figure 4(B), the scanned image is scanned in such a way that a shadow is cast on the edge of the printed material. This is so that the shadow cast on the edge of the printed material, which is pulled into the inspection device 200 and transported on the conveyor belt 402, is illuminated by the skew detection paper irradiation device 412, and the inspection sensor 403 scans this shadow. The skew angle of the printed material is detected using this shadow. Based on the skew angle thus detected, correction processing is performed by the image deformation unit 509, which will be described later.

[0039] The image quality difference adjustment unit 507 is a module that adjusts the image difference between the scanned image and the reference image. The scanned image is image data obtained by printing and scanning the reference image, and even if there are no image defects, there is an image difference between it and the reference image. This difference arises from the effects of image processing before printing, the characteristics of the image forming apparatus 100, and the characteristics of the scanner. Image processing before printing includes color conversion processing, gamma processing, and halftone processing. The characteristics of the image forming apparatus 100 include color reproducibility, dot gain, and gamma characteristics. Scanner characteristics include color reproducibility, S / N ratio, and scanner MTF. In addition, the number of bits between images may differ. Various processing is applied to both or only the reference image to eliminate these effects and, assuming there are no image defects, to eliminate the difference between the scanned image and the reference image. Various processing includes color conversion processing, gamma correction processing, filtering (to adjust for de-screening and edge blurring), and bit width adjustment. Furthermore, there is edge correction processing (thickening processing and smoothing processing) to match the edge reproducibility of the reference image and the scanned image. Details of the edge correction process will be described later in the calibration process section.

[0040] If processing is applied only to the reference image, the simulation will create an image equivalent to the scanned image from the reference image, which is equivalent to simulating the characteristics of the image forming apparatus 100 and inspection sensor 403 in which no image defects occur.

[0041] The resolution conversion unit 508 is a module that converts the resolution of scanned images and reference images. The scanned image and the reference image may have different resolutions when input to the inspection device control unit 405. Also, the resolution used by each module of the inspection processing unit 513 may differ from the input resolution. In such cases, this module performs the resolution conversion. For example, suppose the scanned image is 600 dpi for main scan and 300 dpi for sub-scan, and the reference image is 1200 dpi for main scan and 1200 dpi for sub-scan. If the resolution required by the inspection processing unit 513 is 300 dpi for both main and sub-scan, then the respective image data is scaled down to make both images 300 dpi for both main and sub-scan. The scaling method can be any known method, taking into account the computational load and required precision. For example, scaling using the SINC function is computationally intensive but provides high-precision scaling results. Scaling using the nearest neighbor method is computationally intensive but provides low-precision scaling results.

[0042] The image deformation unit 509 is a module that deforms scanned images and reference images. Geometric differences exist between scanned images and reference images due to paper expansion and contraction during printing, and skew during scanning. The image deformation unit 509 corrects these geometric differences by deforming the image based on information obtained from the skew detection unit 506 and the alignment unit 510, which will be described later. For example, geometric differences can be corrected by linear transformations (rotation, scaling, shearing) and translation. These geometric differences can be expressed as affine transformations, and correction can be performed by obtaining affine transformation parameters from the skew detection unit 506 and the alignment unit 510. Note that the information obtained from the skew detection unit 506 is only parameters related to rotation (skew angle information).

[0043] The alignment unit 510 is a module that performs alignment between a scanned image and a reference image. It is assumed that the scanned image and reference image input to this module are images of the same resolution. Note that the higher the input resolution, the more accurate the alignment becomes, but the greater the computational load. By correcting the image in the image deformation unit 509 based on the parameters obtained from the alignment, it is possible to obtain the scanned image and reference image to be used in the matching unit 511, which will be described later. Various alignment methods can be considered, but in this embodiment, in order to reduce the computational load, a method is used that performs alignment of the entire image using information from a part of the image rather than the entire image. The alignment according to this embodiment consists of three steps: selection of alignment patches, alignment of each patch, and estimation of affine transformation parameters. Each step will be described below.

[0044] First, let's explain the selection of alignment patches. Here, "patch" refers to a rectangular region within an image. In selecting alignment patches, several patches suitable for alignment are chosen from the reference image. Patches with large corner features are considered suitable for alignment. Corner features are features where two distinct edges with different directions exist in a local neighborhood (the intersection of two edges). Corner features are features that represent the strength of these edge features. Various methods have been proposed based on differences in how "edge features" are modeled.

[0045] One known method for calculating corner features is called Harris's corner detection method. Harris's corner detection method calculates a corner feature image from a horizontal differential image (horizontal edge feature image) and a vertical differential image (vertical edge feature image). This corner feature image represents the edge quantity of the weaker of the two edges that make up the corner feature. Since both edges in a corner feature should be strong, the size of the corner feature is expressed by whether the relatively weaker edge has a strong edge quantity. A corner feature image is calculated from a reference image, and the parts with large corner features are selected as patches suitable for alignment. If regions with large corner features are simply selected as patches in order, patches may be selected only from biased regions. In such cases, the number of regions without patches increases, and the image deformation information of those regions cannot be used, making it unsuitable for aligning the entire image.

[0046] Therefore, when selecting patches, we consider not only the size of the corner features but also how the patches are distributed within the image. Specifically, even if the corner features of a candidate patch region are not large within the overall image, if they are large within a local area of ​​the image, the patch will be selected. This makes it possible to distribute patches within the reference image. The parameters for patch selection include the size of the patch and the number (or density) of patches. Larger patches and a greater number of patches improve the accuracy of alignment, but the computational load increases.

[0047] Next, we will explain patch-by-patch alignment. Patch-by-patch alignment involves aligning the alignment patch in the reference image selected in the previous step with the corresponding patch in the scanned image.

[0048] As a result of the alignment process, two types of information are obtained. The first is the center coordinates (refpX_i, reppY_i) of the alignment patch in the i-th reference image (i=1 to N, where N is the number of patches). The second is the position of those center coordinates in the scanned image (scanpX_i, scanpY_i). Any alignment method is acceptable as long as it is a method for estimating the shift amount that can obtain the relationship between (refpX_i, reppY_i) and (scanpX_i, scanpY_i). For example, one method could be to use FFT to place the alignment patch and the corresponding patch in frequency space, calculate their correlation there, and estimate the shift amount.

[0049] Finally, we will explain the estimation of affine transformation parameters. The affine transformation is a coordinate transformation method expressed by the equation shown in Figure 14(C).

[0050] In this equation, there are six types of affine transformation parameters: a, b, c, d, e, and f. Here, (x, y) corresponds to (refpX_i, refpY_i), and (x', y') corresponds to (scanpX_i, scanpY_i). The affine transformation parameters are estimated using this correspondence obtained from N patches. For example, the affine transformation parameters can be determined using the least squares method. Based on the determined affine transformation parameters, the image deformation unit 509 deforms the reference image or scanned image to create an image after alignment correction, which can then be used as a set of reference image and scanned image for use in the matching unit 511.

[0051] The matching unit 511 is a module that performs matching between a scanned image and a reference image. The scanned image and reference image input to this module are image data of the same resolution. Furthermore, it is assumed that the reference image or scanned image has been corrected by the image deformation unit 509 based on information obtained by the alignment unit 510, in order to enable image comparison. The matching unit 511 first creates a difference image between the reference image and the scanned image. This difference image is, for example, The difference image DIF(x,y) = DIS(reference image REF(x,y) - scan image SCAN(x,y)) is calculated. Here, (x,y) represents coordinates, and DIS() is a function that calculates the distance between pixel values. For grayscale images, DIS() can be a simple absolute difference, or a function that calculates the absolute difference considering gamma. For color images, a function that calculates the color difference should be used.

[0052] Next, the pixel values ​​of regions in the obtained difference image that have pixel values ​​below a certain level are set to 0, and this becomes the corrected difference image. This is because pixels in the difference image with pixel values ​​below a certain level are considered acceptable differences that do not constitute image defects. Next, the non-zero pixel regions in the image are concatenated, and the group of pixels enclosed by regions with pixel values ​​of 0 is made into a pixel block. Then, image features are calculated for each of the pixel blocks in the image. Examples of image features include the mean difference value and area. In addition, the variance value may also be calculated. These image features are used by the determination unit 512. The output from the matching unit 511 includes the corrected difference image and information about the pixel blocks (the position and image features of each pixel block).

[0053] The determination unit 512 is a module that determines the presence or absence of image defects from the matching results generated by the matching unit 511. The inputs to this module are the corrected difference image, the position information of the pixel clusters, and the image features, which are the outputs of the matching unit 511. For each pixel cluster, the image features are evaluated to determine whether that pixel cluster is an image defect. For example, if the image features are the area and the average difference value, the determination of whether it is an image defect is made according to the criteria shown in Figure 12(A).

[0054] Figure 12(A) shows the criteria for determining whether an image is defective when the image features are area and mean difference.

[0055] Figure 12(A) shows the area of ​​a pixel cluster on the horizontal axis and the average difference value of the pixel cluster on the vertical axis. The areas indicated as OK (OK areas) are areas without image defects, and the areas marked as NG (NG areas) are areas considered to have image defects. The line segment parameter separating the OK and NG areas is set by the control unit 503 to the determination unit 512. If there are three or more image features to be judged, the control unit 503 sets a plane parameter (or a hyperplane parameter of (image feature dimension - 1 dimension)) that distinguishes the NG areas from the others in the feature space composed of the image features to the determination unit 512. As a result of this judgment, for areas of pixel clusters that were not considered to have image defects, the pixel value in the corrected difference image of that pixel cluster is set to 0. After processing all pixel clusters is completed, the image in which only the areas considered to have image defects have non-zero values ​​becomes the judgment result image. The output from the matching unit 512 is this judgment result image and information about the pixel clusters remaining on the judgment result image.

[0056] The calibration unit 514 determines edge correction parameters to be used in the edge correction processing in the image quality difference adjustment unit 507 in order to match the edge reproducibility of the RIP data (reference image) and the scanned image (image to be inspected). The edge correction parameters are determined based on the reference image used for printing the calibration chart, which will be described later, and the scanned image generated by the image input unit 501 after reading that calibration chart. Details of the calibration process will be described later.

[0057] Next, the calibration chart will be explained with reference to Figure 10.

[0058] Figure 10 shows the patches included in the calibration chart according to Embodiment 1, and an example of the calibration chart.

[0059] Calibration charts consist of vertical or horizontal patch lines to match the edge reproducibility of RIP data (reference image) and scanned images (images under inspection).

[0060] Figure 10(A) shows a vertical line patch 1001, with a size of 70 x 70 pixels. The ratio of black lines to white within the patch is 1:7, i.e., 1 dot and 7 spaces. However, the patch size and the ratio of black lines to spaces (white) are not limited to these. For example, the patch size may be changed depending on the paper size. Alternatively, the ratio of black lines to white may be 2:6, i.e., 2 dots and 6 spaces. Furthermore, not the entire area within the patch is used; the area actually used is the central area 1003 of the patch, shown in gray in the figure.

[0061] Figure 10(B) shows horizontal line patch 1002. The size and shape of horizontal line patch 1002 are the same as those of vertical line patch 1001 in Figure 10(A).

[0062] Figure 10(C) shows a calibration chart with the vertical line patch 1001 from Figure 10(A) and the horizontal line patch 1002 from Figure 10(B) placed on it.

[0063] In Embodiment 1, the chart paper size is A4, and the number of patches is one vertical line patch and one horizontal line patch. The top-left coordinates of vertical line patch 1001 are (X1, Y1), and the top-left coordinates of horizontal line patch 1002 are (X2, Y2). However, the number of vertical and horizontal line patches and their placement are not limited to these. For example, vertical line patches with different ratios of black to white lines may be placed.

[0064] In Figure 10(C), the vertical line patch 1001 and the horizontal line patch 1002 are placed approximately in the center of the chart, but they may also be placed at the edge of the chart. Alternatively, color patches for color correction may be placed in the margin space.

[0065] Next, the UI screen for the calibration process will be explained with reference to Figure 12(B). The user uses the operation unit / display unit 505 to perform calibration settings, calibration execution, and inspection execution according to Embodiment 1.

[0066] Figure 12(B) shows an example of a menu screen displayed on the operation / display unit 505 that instructs calibration settings, calibration execution, and inspection execution.

[0067] When the calibration setting button 1201 is pressed, the system transitions to the calibration setting screen shown in Figure 13(A). When the calibration execution button 1202 is pressed, the calibration is performed. When the inspection execution button 1203 is pressed, the inspection is performed.

[0068] Figure 13(A) shows an example of a calibration settings screen where the user selects the calibration data to be performed.

[0069] The user selects the calibration data to be used from a registered list based on the paper size and type, or registers new data if it is not in the list. Here, the difference in edge reproducibility between the reference image and the scanned image, which is the problem of the present invention, changes depending on the paper size and type, and is therefore listed as an item in the calibration settings, but it is not limited to this. For example, information such as the weight of the paper may also be used. The image data included in the calibration data is the data shown in Figure 10 above.

[0070] When the New Registration button 1301 is pressed on this screen, the system transitions to the calibration registration screen shown in Figure 13(B). When the Delete button 1302 is pressed, the calibration data selected in the list is deleted. When the OK button 1303 is pressed, the calibration data selected in the list can be applied when performing calibration. In other words, calibration can be performed with the selected calibration data. Figure 13(A) shows that "Calibration Data 1" is selected. When the Cancel button 1304 is pressed, the system returns to the screen shown in Figure 12(B).

[0071] Figure 13(B) shows an example of the calibration registration screen for registering new calibration data.

[0072] The user enters a name for the calibration data they wish to register, selects the paper size and paper type, and presses the OK button 1305. In this way, new calibration data can be registered. In the example in Figure 13(B), calibration data named "Calibration Data 3," with paper size A4 and paper type "Thick Paper" is set. If the Cancel button 1306 is pressed, the user returns to the screen in Figure 13(A) without registering the calibration.

[0073] The calibration process according to Embodiment 1 will be described below with reference to Figures 6 to 10, 12 and 13. Here, the calibration process is assumed to be performed before the inspection process. The calibration process may be performed automatically each time before the inspection process, or, as described above in Figure 12(B), it may be performed each time a user instructs it to be performed during the calibration process for the inspection process. Furthermore, it is not limited to this. For example, the calibration process according to the embodiment may be performed using the UI unit 23 of the image forming apparatus 100 as part of a general color adjustment process other than inspection.

[0074] Figure 7 is a flowchart illustrating the process by which the inspection device 200 according to Embodiment 1 generates calibration data and prints a calibration chart. The process shown in this flowchart is achieved by the CPU 515 of the control unit 503 executing a program loaded into the memory unit 504. In this case, the process shown in this flowchart is started when the inspection execution button 1203 is pressed on the menu screen shown in Figure 12(B).

[0075] First, in S701, the CPU515 determines whether calibration data has already been registered. If it has been registered, the process proceeds to S702, where the CPU515 selects the registered calibration data and proceeds to S704. On the other hand, if it has not been registered, the process proceeds to S703, where the CPU515 creates new calibration data according to the user's operation and proceeds to S704.

[0076] In S704, the CPU 515 sends the calibration data selected in S702, or the newly created calibration data in S703, to the image forming apparatus 100. The image forming apparatus 100 then generates RIP data from the calibration data in its image processing unit 105. Next, in S705, the CPU 515 receives the RIP data and uses the printer unit 206 of the image forming apparatus 100 to print according to the RIP data. The resulting printed paper becomes the calibration chart. Here, for example, a calibration chart like the one shown in Figure 10(C) is printed.

[0077] Figure 8 is a flowchart illustrating the process by which the inspection device 200 according to Embodiment 1 reads a calibration chart and obtains patch density from a scanned image. The process shown in this flowchart is achieved by the CPU 515 of the control unit 503 executing a program loaded into the memory unit 504.

[0078] First, in S801, the CPU 515 controls the image input unit 501 to read the calibration chart. Next, in S802, the CPU 515 acquires patch images of vertical and horizontal line patches from the scanned image of the calibration chart. This acquisition of patch images is performed based on the position coordinates where the vertical line patch 1001 and horizontal line patch 1002 are placed, as shown in Figure 10(C) above. Then, in S803, the CPU 515 acquires the patch density from the patch image acquired in S802. Here, the patch density is the average density in the reference region 1003 shown in Figures 10(A) and (B) above.

[0079] Figure 11(A) is an enlarged schematic diagram of the reference region 1003 of the patch image 1001 obtained by reading the calibration chart with S802.

[0080] In Figure 11(A), reference region 1003 contains four vertical lines, each of which appears blurred and thickened due to the reading process. The patch density in Figure 11(A) is assumed to be 15%.

[0081] Figure 9 is a flowchart illustrating the process by which the inspection device 200 according to Embodiment 1 determines the edge correction parameters. The process shown in this flowchart is achieved by the CPU 515 of the control unit 503 executing a program loaded into the memory unit 504.

[0082] First, in S901, the CPU 515 acquires the RIP data generated in S704 by the image forming apparatus 100. Next, in S902, the CPU 515 acquires patch images of vertical line patches and / or horizontal line patches from the acquired RIP data. This acquisition of patch images is performed based on the position coordinates where the vertical line patch 1001 and horizontal line patch 1002 are placed, as shown in Figure 10(C) above.

[0083] Figure 11(B) shows a magnified schematic diagram of the reference area of ​​the patch image acquired by S902. Here, since the patch image is acquired from RIP data, each thin line is formed with a width of 1 pixel.

[0084] Next, in S903, the CPU 515 performs edge correction on the patch image acquired in S902 using the calibration unit 514. This edge correction involves gradually thickening thin lines, followed by smoothing each line. The parameters used for this edge correction are referred to as edge correction parameter 1, edge correction parameter 2, ..., edge correction parameter N, in order from thinnest to thickest. The smoothing process is fixed.

[0085] For example, edge correction parameter 1 performs only smoothing without thickening. Edge correction parameter 2 performs smoothing on the result after thickening by one step. Edge correction parameter 3 performs smoothing on the result after thickening by two steps. Similarly thereafter, edge correction parameter N performs smoothing on the result after thickening by (N-1) steps.

[0086] Here, any known method can be used to thicken the thin lines. For example, one method is to perform a first smoothing process on the entire patch image, and then perform a first edge enhancement to thicken the thin lines. When the entire patch image is smoothed, the edges of the thin lines become blurred, so the thin lines can be thickened by performing edge enhancement afterward. Furthermore, if a stronger second smoothing process is performed, the edges of the thin lines become even more blurred, so the thin lines can be thickened even further by performing a stronger second edge enhancement afterward. In this method, since the processing is performed on the entire image, attribute information and edge detection are unnecessary. Alternatively, as another method for thickening thin lines, for example, in the patch image, after edge detection of the thin lines, the edges can be simply thickened to widths of 2 pixels, 3 pixels, ..., N pixels.

[0087] Figure 15(B) shows the relationship between the edge correction parameters, thickening process, and smoothing process according to Embodiment 1. In Figure 15(B), edge correction parameter 1 performs only the smoothing process without thickening. Then, for edge correction parameters 2 and beyond, the amount of thickening of the thin lines is increased in order.

[0088] Figure 11(C) shows the result of thickening the thin lines by one step using edge correction parameter 2 in relation to Figure 11(B), and Figure 11(D) shows the result of thickening the thin lines by two steps using edge correction parameter 3.

[0089] Furthermore, the results of applying smoothing processing to Figures 11(B) to 11(D), respectively, are shown in Figures 11(E) to 11(G). For the smoothing processing, known methods such as smoothing filters can be used.

[0090] Next, the process proceeds to S904, where the CPU 515 uses the calibration unit 514 to obtain the patch density from the patch image edge-corrected in S903. Here, as shown in Figure 11(E), the patch density is 5% with edge correction parameter 1; as shown in Figure 11(F), the patch density is 10% with edge correction parameter 2; and as shown in Figure 11(G), the patch density is 16% with edge correction parameter 3.

[0091] Next, proceeding to S905, the CPU 515 uses the calibration unit 514 to acquire edge correction parameters based on the density of the patch image obtained by reading the calibration chart in S803 and the density of the patch image after edge correction on the RIP data in S904. Specifically, it acquires the edge correction parameter that has the smallest difference (or the first value exceeded) from the density of the patch image obtained in S803.

[0092] For example, in the example in Figure 11, the patch density in Figure 11(A), which corresponds to the density of the patch image acquired by S803, is 15%. Therefore, the closest value in Figures 11(E) to (G) is the patch density of 16% in Figure 11(G). Accordingly, edge correction parameter 3, which corresponds to Figure 11(G), is acquired as the edge correction parameter.

[0093] Next, the process proceeds to S906, where the CPU 515 saves the edge correction parameters acquired in S905 to the memory unit 504 and terminates this process. In S906, the edge correction parameters are saved in association with the calibration data, paper size, and paper type. Then, in the pre-processing (edge ​​correction processing) for the inspection process described later, the appropriate edge correction parameters will be applied based on the paper size and paper type conditions. For example, if the edge correction parameters are saved as shown in Figure 15(C), then if the paper size is A4 and the paper type is cardboard, edge correction parameter 3 will be applied in the pre-processing (edge ​​correction processing) for the inspection process.

[0094] In this way, by applying edge correction parameters to the RIP data (reference image), the RIP data (reference image) can be adjusted to match the edge reproducibility of the scanned image. Next, the inspection process by the inspection device 200 according to Embodiment 1 will be explained with reference to the flowchart in Figure 6. This inspection process is performed after the calibration process described above has been completed.

[0095] Figure 6 is a flowchart illustrating the inspection process during inspection by the inspection device 200 according to Embodiment 1. The process shown in this flowchart is achieved by the CPU 515 of the control unit 503 executing a program loaded into the memory unit 504. The results of the process in this flowchart are stored in the memory unit 504 and used in subsequent processing.

[0096] First, in S601, the CPU 515 performs pre-processing for the inspection process. At this time, the CPU 515 uses the inspection control information received from the image forming apparatus 100, which is stored in the memory unit 504 via the communication unit 502, to select an image pair of the scanned image to be inspected and a reference image. The CPU 515 then processes the scanned image with the oblique detection unit 506 to obtain the tilt information of the scanned image. Based on this tilt information, the image deformation unit 509 performs correction processing on the scanned image. In parallel with this, the reference image is processed by the image quality difference adjustment unit 507 to make it an image suitable for inspection processing, as described above. Here, edge correction processing is performed on the RIP data (reference image) using the edge correction parameters obtained in the calibration process described above.

[0097] For example, in Embodiment 1, since edge correction parameter 3 is acquired as an edge correction parameter, the image quality difference adjustment unit 507 performs smoothing on the result of thickening the thin lines in two steps. This makes it possible to match the edges of the RIP data (reference image) to the reproducibility of the edges of the scanned image. In this case, for example, as shown in Figure 15(C), if multiple edge correction parameters are stored in association with the calibration data, the edge correction parameter will be selected according to the size and type of the sheet to be inspected and applied in the pre-processing (edge ​​correction processing) of the inspection process.

[0098] Next, the process proceeds to S602, where the CPU 515 performs alignment using the scanned image and reference image obtained in S601. At this time, the CPU 515 first converts the scanned image and reference image to a predetermined resolution (for example, 300 dpi × 300 dpi) using the resolution conversion unit 508. Then, the alignment unit 510 processes the scanned image and reference image converted to the predetermined resolution to obtain affine transformation parameters. Finally, the CPU 515 uses the affine transformation parameters obtained from the alignment unit 510 to perform correction processing on the reference image using the image deformation unit 509, making the coordinate system the same as the scanned image, and creating an image that can be used for matching.

[0099] Then, proceeding to S603, the CPU 515 performs a comparison / determination process using the scanned image and reference image obtained in S602. First, the CPU 515 processes the scanned image and reference image in the comparison unit 511. Then, using the results from the comparison unit 511, the determination unit 512 performs a determination process. The determination unit 512 performs its processing by setting predetermined judgment criteria, which are set in advance by the operation unit / display unit 505, in the determination unit 512.

[0100] Then, proceeding to S604, the CPU 515 displays the inspection results on the operation / display unit 505. At this point, simply displaying the final judgment result image would make it difficult to understand what kind of image defect there was, so the final judgment result image is superimposed on the scanned image and displayed on the operation / display unit 505. Any superimposing method is acceptable as long as it makes it easy to identify the location of the image defect. For example, the discrepancies in the final judgment result image may be displayed in red on the scanned image.

[0101] As described above, according to Embodiment 1, false positives can be reduced by matching the edge reproducibility of the RIP data (reference image) and the scanned image (image to be inspected).

[0102] In Embodiment 1, we described how to gradually thicken and smooth the RIP data (reference image) to obtain edge correction parameters that match the edge reproducibility of the scanned image. However, conversely, the scanned image may be gradually enhanced to obtain edge correction parameters that match the edge reproducibility of the RIP data (reference image). However, there are limitations to the enhancement process applied to the scanned image, and it will not produce lines like those in the RIP data (reference image). Therefore, it is conceivable to use both edge correction parameters for the scanned image and edge correction parameters for the RIP data (reference image).

[0103] [Embodiment 2] In the above-described embodiment 1, we explained how to obtain edge correction parameters by gradually thickening and smoothing the RIP data to match the patch density of the scanned image.

[0104] In contrast, Embodiment 2 describes a method in which the Modulated Transfer Function (MTF) of the scanned image is obtained, and a filtering process for the RIP data is selected according to the MTF. This makes it possible to omit processes such as gradually increasing the thickness of the RIP data, smoothing, and obtaining patch density, and replace them with a single filtering process.

[0105] Embodiment 2 will be described below with reference to Figures 14 and 15(A). Note that in Embodiment 2, the same configurations, processes, and figures as in Embodiment 1 will not be explained.

[0106] Figures 14(A) and 14(B) are enlarged schematic diagrams of the reference area of ​​the patch image acquired by scanning in S802 of Embodiment 1. Figure 15(A) is a diagram showing the frequency characteristics of the filters pre-stored in the memory unit 504. Filter 1 is a filter that attenuates to 80%, and filter 2 is a filter that attenuates to 60%.

[0107] Figure 14(A) shows an example of a patch image acquired under the conditions of A4 size, plain paper.

[0108] This indicates that the average density in the center of the thin wire is 90%, and the average density at the ends of the thin wire is 10%. Here, the MTF is 80% (=90-10), so under these conditions, filter 1, which has an attenuation of 80% corresponding to the MTF, is selected as shown in Figure 15(A).

[0109] Figure 14(B) shows an example of a patch image acquired under the conditions of A4 size, thick paper. It shows that the average density at the center of the thin lines is 70%, and the average density at the ends of the thin lines is 10%. Here, the MTF is 60% (=70-10), so under these conditions, filter 2, which has an attenuation of 60% corresponding to the MTF, is selected as shown in Figure 15(A).

[0110] The filter thus selected is used as a filter on the RIP data (reference image) in the edge correction processing of the image quality difference adjustment unit 507, similar to Embodiment 1.

[0111] Furthermore, since there are four thin lines within the reference area of ​​Figure 14(A)(B), the density at the center of the thin line and the density at the ends of the thin line may be the average of the four thin lines.

[0112] As described above, according to Embodiment 2, the MTF of the scanned image is obtained, and a filter process for the RIP data is selected according to the MTF. This makes it possible to match the edge reproducibility of the RIP data (reference image) and the scanned image (image to be inspected) with a simple process.

[0113] (Other embodiments) The present invention can also be realized by supplying a program that implements one or more of the functions of the above-described embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of that system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that implements one or more functions.

[0114] The present invention is not limited to the embodiments described above, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, the following claims are attached to make the scope of the invention public. [Explanation of Symbols]

[0115] 100…Image forming apparatus, 200…Inspection apparatus, 300…Finisher, 403…Inspection sensor, 501…Image input unit, 503…Control unit, 504…Memory unit, 505…Operation unit / Display unit, 507…Image quality difference adjustment unit, 508…Resolution conversion unit, 509…Image deformation unit, 510…Alignment unit, 511…Verification unit, 512…Determination unit, 514…Calibration unit, 515…CPU

Claims

1. An inspection device that detects the difference between image data obtained by reading a sheet and a reference image of said image data, A reading means that reads a sheet on which a patch image is printed and obtains first image data, A first acquisition means for acquiring a second image data from the print data of the aforementioned patch image, A correction means that performs edge correction by applying edge correction parameters to lines included in the second image data, A second acquisition means for acquiring edge correction parameters that minimize the difference between the density of the second image data, which has been edge-corrected by the correction means, and the density of the first image data. A storage means for storing edge correction parameters acquired by the second acquisition means, The aforementioned patch image includes thin lines, The edge correction parameters include settings related to the process of thickening the thin lines and the process of smoothing them. An inspection apparatus characterized in that the density of the second image data and the density of the first image data are the average density of a reference region containing a plurality of the fine lines in the patch image.

2. The inspection apparatus according to claim 1, characterized in that the thin line includes a plurality of vertical and horizontal lines.

3. A registration means for registering calibration data for printing the aforementioned patch image, The system further includes means for selecting calibration data registered in the registration means and causing the printing device to print it, The inspection apparatus according to claim 1 or 2, characterized in that the sheet on which the patch image is printed is printed by the printing apparatus.

4. The inspection apparatus according to claim 3, characterized in that the calibration data includes image data of the patch image, the size and type of the sheet.

5. The inspection apparatus according to claim 3 or 4, further characterized in that the storage means stores in association the calibration data used to print the patch image and the edge correction parameters acquired by the second acquisition means.

6. An inspection device that detects the difference between image data obtained by reading a sheet and a reference image of said image data, A reading means that reads a sheet on which a patch image is printed and obtains first image data, A first acquisition means for acquiring a second image data from the print data of the aforementioned patch image, A second acquisition means for acquiring edge correction parameters that minimize the difference between the density of line edges included in the first image data and the density of line edges included in the second image data, A correction means that performs edge correction on the reference image using the edge correction parameters acquired by the second acquisition means, A matching means for comparing the image data of the object to be inspected obtained by the reading means by reading the sheet with a reference image corrected by the correction means, An inspection device characterized by having the following features.

7. The inspection apparatus according to claim 6, characterized in that the second acquisition means acquires an edge correction parameter that minimizes the difference between the density of the first image data and the density of the image data after edge correction by the correction means by applying the edge correction parameter to the lines contained in the second image data.

8. An inspection device that detects the difference between image data of an object to be inspected obtained by reading a sheet and a reference image of the image data of the object to be inspected, A storage means for storing edge correction parameters that minimize the difference between the density of first image data obtained by reading a sheet on which a patch image is printed and the density of second image data obtained from the print data of the patch image, During sheet inspection, a selection means selects edge correction parameters to correct the reference image from the edge correction parameters stored in the storage means, Correction means for correcting the reference image of the image data to be inspected, or the first image data, using the edge correction parameters selected by the selection means, A comparison means for comparing the reference image corrected by the correction means with the image data of the object to be inspected, or the reference image with the first image data corrected by the correction means, An inspection device characterized by having the following features.

9. The storage means stores the size or type of the sheet in association with the edge correction parameter. The inspection apparatus according to claim 8, characterized in that the selection means selects the edge correction parameter based on the size or type of the sheet to be inspected.

10. An inspection device that detects the difference between image data of an object to be inspected obtained by reading a sheet and a reference image of the image data of the object to be inspected, A reading means that reads a sheet on which a patch image is printed and obtains first image data, An acquisition means for acquiring a second image data from the print data of the aforementioned patch image, Correction means for correcting the second image data by selecting a filter process for the second image data based on the MTF of the first image data, A detection means for detecting the difference by comparing the first image data with the second image data corrected by the correction means, An inspection device characterized by having the following features.

11. The inspection apparatus according to claim 10, characterized in that the correction means selects a filter processing of attenuation amount corresponding to the MTF to correct the second image data.

12. A control method for controlling an inspection device that detects the difference between image data obtained by reading a sheet and a reference image of said image data, A reading process involves reading a sheet on which a patch image is printed to obtain the first image data, A first acquisition step involves obtaining a second image data from the print data of the aforementioned patch image, A correction step which involves applying edge correction parameters to lines included in the second image data to perform edge correction, A second acquisition step involves acquiring edge correction parameters that minimize the difference between the density of the second image data edge-corrected by the correction step and the density of the first image data. The system includes a storage step for storing the edge correction parameters acquired in the second acquisition step, The aforementioned patch image includes thin lines, The edge correction parameters include settings related to the process of thickening the thin lines and the process of smoothing them. A control method characterized in that the density of the second image data and the density of the first image data are the average density of a reference region containing a plurality of the thin lines in the patch image.

13. A control method for controlling an inspection device that detects the difference between image data obtained by reading a sheet and a reference image of said image data, A reading process involves reading a sheet on which a patch image is printed to obtain the first image data, A first acquisition step involves obtaining a second image data from the print data of the aforementioned patch image, A second acquisition step involves obtaining edge correction parameters that minimize the difference between the density of the line edges included in the first image data and the density of the line edges included in the second image data. A correction step in which edge correction is performed on the reference image using the edge correction parameters obtained in the second acquisition step, A comparison step involves comparing the image data of the object to be inspected, obtained by reading the sheet, with a reference image corrected by the correction step, A control method characterized by having the following features.

14. A control method for controlling an inspection device that detects the difference between image data of an object to be inspected obtained by reading a sheet and a reference image of the image data of the object to be inspected, comprising a storage means for storing edge correction parameters that minimize the difference between the density of first image data obtained by reading a sheet on which a patch image is printed and the density of second image data obtained from the print data of the patch image, During sheet inspection, a selection step is made to select edge correction parameters for correcting the reference image from the edge correction parameters stored in the storage means, A correction step is performed to correct the reference image of the image data to be inspected, or the first image data, using the edge correction parameters selected in the selection step. A comparison step of comparing the reference image corrected by the correction step with the image data of the object to be inspected, or the reference image with the first image data corrected by the correction step, A control method characterized by having the following features.

15. A control method for controlling an inspection device that detects the difference between image data of an object to be inspected obtained by reading a sheet and a reference image of the image data of the object to be inspected, A reading process involves reading a sheet on which a patch image is printed to obtain the first image data, An acquisition step of acquiring a second image data from the print data of the aforementioned patch image, A correction step in which a filter process is selected for the second image data based on the MTF of the first image data to correct the second image data, A detection step that compares the first image data with the second image data corrected by the correction step to detect the difference, A control method characterized by having the following features.

16. A program for causing a computer to perform all of the steps of the control method described in any one of claims 12 to 15.

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