Patent Publication Number: US-11386538-B2

Title: Image processing apparatus, image processing method, and storage medium

Description:
This application is a National Stage Entry of PCT/JP2019/001616 filed on Jan. 21, 2019, which claims priority from Japanese Patent Application 2018-012307 filed on Jan. 29, 2018, the contents of all of which are incorporated herein by reference, in their entirety. 
     TECHNICAL FIELD 
     The present invention relates to an image processing apparatus, an image processing method, and a storage medium. 
     BACKGROUND ART 
     Patent Literature 1 discloses an image component separation apparatus that separates an abnormal component in an input medical image. The image component separation apparatus disclosed in Patent Literature 1 generates, from an input medical image representing a predetermined structure in a subject, a normal image representing normal structure of the structure in the subject. Then, a difference between the input medical image and the normal image is calculated to separate an abnormal component in the input medical image. 
     CITATION LIST 
     Patent Literature 
     PTL 1: Japanese Patent No. 4895204 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, the image component separation apparatus disclosed in Patent Literature 1 generates a normal image from the whole input medical image representing a predetermined structure in the subject. Thus, in the image component separation apparatus disclosed in Patent Literature 1, since the generated normal image may be affected by the whole input medical image, separation of an abnormal component may be significantly affected by an individual difference of the input medical images. 
     In view of the problem described above, the present invention intends to provide an image processing apparatus, an image processing method, and a storage medium that can distinguish an anomaly while reducing influence of an individual difference of images. 
     Solution to Problem 
     According to one example aspect of the present invention, provided is an image processing apparatus including: a first generation unit that generates a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target; a second generation unit that estimates a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image; a comparison unit that compares the first estimation image with the inspection image; and an output unit that outputs a comparison result obtained by the comparison unit, and the comparison unit compares a difference between the first estimation image and the inspection image with the second estimation image. 
     According to another example aspect of the present invention, provided is an image processing method including: a first generation step of generating a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target; a second generation step of estimating a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image; a comparison step of comparing the first estimation image with the inspection image; and an output step of outputting a comparison result obtained by the comparison step, and the comparison step compares a difference between the first estimation image and the inspection image with the second estimation image. 
     According to yet another example aspect of the present invention, provided is a storage medium storing a program that causes a computer to perform an image processing method including: a first generation step of generating a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target; a second generation step of estimating a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image; a comparison step of comparing the first estimation image with the inspection image; and an output step of outputting a comparison result obtained by the comparison step, and the comparison step compares a difference between the first estimation image and the inspection image with the second estimation image. 
     According to yet another example aspect of the present invention, provided is an image processing apparatus including: a first generation unit that generates a second estimation image including at least a predetermined region of an object by using a part of a first image including the object; a second generation unit that estimates a difference between the second image and the first image to generate a third image by using the part of the first image; a comparison unit that compares the second image with the first image; and an output unit that outputs a comparison result obtained by the comparison unit, and the comparison unit compares a difference between the second image and the first image with the third image. 
     Advantageous Effects of Invention 
     According to the present invention, it is possible to distinguish an anomaly at high accuracy while reducing influence of an individual difference of images. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a function configuration of an image processing apparatus according to one example embodiment of the present invention. 
         FIG. 2  is a block diagram illustrating a hardware configuration example of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 3  is a flowchart illustrating a patch cut step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 4  is a schematic diagram ( 1 ) illustrating the patch cut step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 5  is a schematic diagram ( 2 ) illustrating the patch cut step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 6  is a schematic diagram ( 3 ) illustrating the patch cut step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 7  is a flowchart illustrating a normal image learning step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 8  is a schematic diagram ( 1 ) illustrating the normal image learning step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 9  is a schematic diagram ( 2 ) illustrating the normal image learning step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 10  is a flowchart illustrating a normal image generation step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 11  is a schematic diagram illustrating the normal image generation step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 12  is a flowchart illustrating a difference image learning step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 13  is a schematic diagram illustrating the difference image learning step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 14  is a flowchart illustrating a defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 15  is a schematic diagram ( 1 ) illustrating the defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 16  is a schematic diagram ( 2 ) illustrating the defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 17  is a schematic diagram illustrating a case where a threshold is fixed in the defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 18  is a schematic diagram ( 1 ) illustrating the relationship between a threshold and a range of values that may be taken by a pixel value of a normal image in the defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 19  is a schematic diagram ( 2 ) illustrating the relationship between a threshold and a range of values that may be taken by a pixel value of a normal image in the defect article detection step in the operation of the image processing apparatus according to one example embodiment of the present invention. 
         FIG. 20  is a block diagram illustrating a function configuration of an image processing apparatus according to a modified example of one example embodiment of the present invention. 
         FIG. 21  is a block diagram illustrating a function configuration of an image processing apparatus according to another example embodiment of the present invention. 
         FIG. 22  is a block diagram illustrating a function configuration of an image processing apparatus according to yet another example embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Example Embodiment 
     An image processing apparatus and an image processing method according to one example embodiment of the present invention will be described with reference to  FIG. 1  to  FIG. 20 . 
     First, the configuration of the image processing apparatus according to the present example embodiment will be described with reference to  FIG. 1  and  FIG. 2 .  FIG. 1  is a block diagram illustrating a function configuration of the image processing apparatus according to the present example embodiment.  FIG. 2  is a block diagram illustrating a hardware configuration example of the image processing apparatus according to the present example embodiment. 
     In the present example embodiment, a case where the image processing apparatus processes an inspection image including an inspection target article that is an inspection target, thereby determines whether the inspection target article is a normal article or a defect article, and performs inspection to detect a defect article as an anomaly will be described as an example. Note that the image processing apparatus according to the present example embodiment can be widely used not only for performing inspection to detect a defect article but also for detecting an anomaly for an object. 
     As illustrated in  FIG. 1 , an image processing apparatus  100  according to the present example embodiment has a learning data storing unit  10 , a patch cut unit  12 , and a patch-processed data storing unit  14 . Further, the image processing apparatus  100  has a normal image learning unit  16  and a normal image learning model storage unit  18 . Further, the image processing apparatus  100  has an inspection data storing unit  20 , a normal image generation unit  22 , and a generated normal data storing unit  24 . 
     Further, the image processing apparatus  100  according to the present example embodiment has a difference calculation unit  26 , a difference image learning unit  28 , a difference image learning model storage unit  30 , a difference image generation unit  32 , a threshold setting unit  34 , and a defect article detection unit  36 . 
     The learning data storing unit  10  stores a learning image used for learning performed by the normal image learning unit  16 . The learning image is an image including a normal article of an inspection target article, that is, an image representing a normal state of the inspection target article. 
     The patch cut unit  12  reads learning image data from the learning data storing unit  10  and performs a patch cut step on the learning image. That is, the patch cut unit  12  cuts out a patch size image as a patch image from a learning image. Further, the patch cut unit  12  cuts out an image of the center part as a center image from the patch image. The center image includes at least a predetermined region of an inspection target article. The patch cut unit  12  outputs a pair of a center-removed patch image, which is a patch image from which the center image has been cut out, and the center image thereof in association with each other as an image pair. The patch cut unit  12  stores the image pair output for the learning image in the patch-processed data storing unit  14 . 
     Further, the patch cut unit  12  reads inspection image data from the inspection data storing unit  20  and performs a patch cut process on an inspection image as with the case of the learning image. The patch cut unit  12  outputs a pair of a center-removed patch image and the center image thereof in association with each other as an image pair for the inspection image as with the case of the learning image. The patch cut unit  12  stores the image pair output for the inspection image in the patch-processed data storing unit  14 . 
     The patch-processed data storing unit  14  stores an image pair for a learning image output by the patch cut unit  12 . Further, the patch-processed data storing unit  14  stores an image pair for an inspection image output by the patch cut unit  12 . 
     The normal image learning unit  16  reads an image pair for a learning image from the patch-processed data storing unit  14  and creates a learning model by using the read image pair. The normal image learning unit  16  performs learning by using a center-removed patch image as learning data and using a center image as training data out of the image pair for a learning image. Thereby, the normal image learning unit  16  creates a normal image learning model that is a learning model used for restoring the center image from the center-removed patch image. The normal image learning unit  16  stores the created normal image learning model in the normal image learning model storage unit  18 . 
     The normal image learning model storage unit  18  stores a trained normal image learning model created as a result of learning performed by the normal image learning unit  16 . 
     The inspection data storing unit  20  stores an inspection image. The inspection image is an image including an inspection target article that is an inspection target. The inspection target article is not particularly limited. The image processing apparatus according to the present example embodiment can define any object such as a completed article or a component, for example, as an inspection target article. 
     The normal image generation unit  22  functions as a first generation unit, which estimates, from a center-removed patch image out of an image pair for a learning image or an inspection image, a center image that is an image of the center part thereof and generates, as a normal image, an estimation image estimated by the center-removed patch image. The normal image includes at least a predetermined region of an inspection target article included in a learning image or an inspection image. The normal image generation unit  22  reads a center-removed patch image out of an image pair for a learning image or an inspection image from the patch-processed data storing unit  14 . The normal image generation unit  22  estimates a center image of a center-removed patch image from the read center-removed patch image and generates it as a normal image. 
     When generating the normal image that is an estimation image, the normal image generation unit  22  reads the trained normal image learning model from the normal image learning model storage unit  18 . The normal image generation unit  22  estimates a center image from a center-removed patch image that is a part of a patch image in a learning image or an inspection image by using the read normal image learning model and generates the estimated estimation image as a normal image. The normal image generation unit  22  stores the generated normal image in the generated normal data storing unit  24 . 
     In such a way, the normal image generation unit  22  estimates and generates a normal image by using a center-removed patch image that is a part of a patch image for a learning image or an inspection image. The normal image generated for a learning image is used for threshold setting described later. Further, the normal image generated for an inspection image is used for defect detection described later. 
     The generated normal data storing unit  24  stores the normal image generated by the normal image generation unit  22 . 
     The difference calculation unit  26  calculates a difference between a normal image generated by the normal image generation unit  22  from a center-removed patch image out of an image pair for a learning image and a center image paired with the center-removed patch image to form the image pair. Thereby, for the learning image, the difference calculation unit  26  generates a difference image in which the absolute value of the difference in pixel values for each pixel between the center image and the normal image is the pixel value. Note that the calculation method of a difference performed by the difference calculation unit  26  is the same as the calculation method of a difference performed by the defect article detection unit  36  described later. 
     The difference image learning unit  28  creates a learning model by using a difference image generated by the difference calculation unit  26  and a center-removed patch image used in the estimation of the normal image from which the difference image has been generated. The difference image learning unit  28  performs learning by using a center-removed patch image as learning data and using a difference image as training data. Thereby, the difference image learning unit  28  creates a difference image learning model that is a learning model that restores a difference image from a center-removed patch image. The difference image learning unit  28  stores the created difference image learning model in the difference image learning model storage unit  30 . 
     The difference image learning model storage unit  30  stores a trained difference image learning model created as a result of learning performed by the difference image learning unit  28 . 
     The difference image generation unit  32  functions as a second generation unit, which estimates and generates a difference image from a center-removed patch image out of an image pair for an inspection image. The difference image generation unit  32  reads the center-removed patch image out of the image pair for the inspection image from the patch-processed data storing unit  14 . The difference image generation unit  32  estimates and generates a difference image from the read center-removed patch image. When generating the difference image that is the estimation image, the difference image generation unit  32  reads a trained difference image learning model from the difference image learning model storage unit  30 . The difference image generation unit  32  uses the read difference image learning model to estimate and generate a difference image from the center-removed patch image that is a part of the patch image in the inspection image. 
     The threshold setting unit  34  sets a threshold used for defect detection in the defect article detection unit  36  based on the difference image generated by the difference image generation unit  32 . The threshold setting unit  34  can set each pixel value of a difference image, for example, as a threshold for each pixel. Further, the threshold setting unit  34  can set a value obtained by multiplying each pixel value of a difference image by a constant multiplying factor and adding a margin, for example, as a threshold for each pixel. 
     The defect article detection unit  36  functions as a comparison unit, which compares a center image out of an image pair for an inspection image with a normal image generated from a center-removed patch image thereof and detects a defect based on the threshold set by the threshold setting unit  34 . Moreover, the defect article detection unit  36  functions as an output unit, which outputs a detection result of a defect. Further, the defect article detection unit  36  functions as a determination unit, which determines whether an inspection target article included in an inspection image is a normal article or a defect article based on the detection result. 
     The defect article detection unit  36  reads a center image out of an image pair for the inspection image from the patch-processed data storing unit  14 . Further, the defect article detection unit reads, from the generated normal data storing unit  24 , a normal image generated from a center-removed patch image out of the image pair. The defect article detection unit  36  calculates a difference between each read center image and a normal image. Accordingly, for an inspection image, the defect article detection unit  36  generates a difference image in which the absolute value of a difference in pixel values on a pixel basis between the center image and the normal image is the pixel value 
     The defect article detection unit  36  compares a difference image generated for an inspection image with a difference image generated by the difference image generation unit  32 . More specifically, the defect article detection unit  36  compares a pixel value of a difference image generated for an inspection image with a threshold set by the threshold setting unit  34  on a pixel basis and detects a defective pixel in the center image for the inspection image. The defect article detection unit  36  determines the quality of an inspection target article as to whether an inspection target article included in an inspection image is a normal article or a defect article based on the detected defect pixel and determines and detects a defect article that is an anomaly. 
     The defect article detection unit  36  outputs a detection result of a defect article. An output method of the detection result is not particularly limited, and various methods may be used. For example, the defect article detection unit  36  can cause a display apparatus to display a detection result, output the detection result as a voice from an audio output apparatus, and store the detection result in a database stored in a storage apparatus. 
     The image processing apparatus  100  described above is formed of a computer apparatus, for example. An example of a hardware configuration of the image processing apparatus  100  will be described with reference to  FIG. 2 . Note that the image processing apparatus  100  may be formed of a single apparatus or may be formed of two or more physically separated apparatuses connected by wired or wireless connection. 
     As illustrated in  FIG. 2 , the image processing apparatus  100  has a central processing unit (CPU)  1002 , a read only memory (ROM)  1004 , a random access memory (RAM)  1006 , and a hard disk drive (HDD)  1008 . Further, the image processing apparatus  100  has an output device  1010  and an input device  1012 . The CPU  1002 , the ROM  1004 , the RAM  1006 , the HDD  1008 , the output device  1010 , and the input device  1012  are connected to a common bus line  1014 . 
     The CPU  1002  controls the overall operation of the image processing apparatus  100 . Further, the CPU  1002  executes a program to realize functions of each unit of the patch cut unit  12 , the normal image learning unit  16 , the normal image generation unit  22 , the difference calculation unit  26 , the difference image learning unit  28 , the difference image generation unit  32 , the threshold setting unit  34 , and the defect article detection unit  36  described above. The CPU  1002  implements functions of each unit of the patch cut unit  12 , the normal image learning unit  16 , and the normal image generation unit  22  by loading a program stored in the HDD  1008  or the like to the RAM  1006  and executing the program. Further, the CPU  1002  implements functions of each unit of the difference calculation unit  26 , the difference image learning unit  28 , the difference image generation unit  32 , the threshold setting unit  34 , and the defect article detection unit  36  by loading a program stored in the HDD  1008  or the like to the RAM  1006  and executing the program. 
     Note that the patch cut unit  12 , the normal image learning unit  16 , the normal image generation unit  22 , the difference calculation unit  26 , the difference image learning unit  28 , the difference image generation unit  32 , the threshold setting unit  34 , and the defect article detection unit  36  may be implemented by a circuitry, respectively. Herein, the circuitry is a term conceptually including a single device, multiple devices, a chipset, or a cloud. 
     A program such as a boot program is stored in the ROM  1004 . The RAM  1006  is used as a working area when the CPU  1002  executes a program. Further, a program executed by the CPU  1002  is stored in the HDD  1008 . 
     Further, the HDD  1008  is a storage device that implements functions of each unit of the above learning data storing unit  10 , the patch-processed data storing unit  14 , the normal image learning model storage unit  18 , the generated normal data storing unit  24 , and the difference image learning model storage unit  30 . Note that a storage device that implements functions of each unit of the learning data storing unit  10 , the patch-processed data storing unit  14 , the normal image learning model storage unit  18 , the generated normal data storing unit  24 , and the difference image learning model storage unit  30  is not limited to the HDD  1008 . Various storage devices can be used for implementing functions of respective units. 
     The output device  1010  is a device that outputs a result of inspection performed by the defect article detection unit  36  and may be, for example, a display device or an audio output device. 
     The input device  1012  is a keyboard, a mouse, or the like, for example. Further, the input device  1012  may be a touch panel embedded in a display device that is the output device  1010 . An operator of the image processing apparatus  100  can set the image processing apparatus  100  via the input device  1012  or can input an instruction of performing a process. 
     Note that a hardware configuration of the image processing apparatus  100  is not limited to the configuration described above, and various configuration can be used. 
     Next, an operation of the image processing apparatus  100  according to the above present example embodiment will be further described with reference to  FIG. 3  to  FIG. 16 . When operating, the image processing apparatus  100  according to the present example embodiment performs an image processing method. 
     The operation of the image processing apparatus  100  according to the present example embodiment includes a patch cut step (see  FIG. 3 ), a normal image learning step (see  FIG. 7 ), and a normal image generation step (see  FIG. 10 ). Further, the operation of the image processing apparatus  100  includes a difference image learning step (see  FIG. 12 ) and a defect article detection step (see  FIG. 14 ). The patch cut step is performed by the patch cut unit  12 . The normal image learning step is performed by the normal image learning unit  16 . The normal image generation step is performed by the normal image generation unit  22 . The difference image learning step is performed by the difference image learning unit  28 . The defect article detection step is performed by the defect article detection unit  36 . 
     First, the patch cut step in the operation of the image processing apparatus  100  according to the present example embodiment will be described with reference to  FIG. 3  to  FIG. 6 .  FIG. 3  is a flowchart illustrating the patch cut step in the operation of the image processing apparatus  100  according to the present example embodiment.  FIG. 4  to  FIG. 6  are schematic diagrams illustrating the patch cut step in the operation of the image processing apparatus  100  according to the present example embodiment. 
     The patch cut step performed by the patch cut unit  12  performs cutting of a patch image from an image, removal of an image of a center part of the patch image, or the like. The patch cut step is performed for each of a learning image and an inspection image. 
     First, as illustrated in  FIG. 3 , in step S 101 , the patch cut unit  12  reads data of an image on which the patch cut step is to be performed. When the patch cut step is performed on the learning image, the patch cut unit  12  reads the learning image from the learning data storing unit  10 . On the other hand, when the patch cut step is performed on the inspection image, the patch cut unit  12  reads image data from the inspection data storing unit  20 . 
     Next, in step S 102 , the patch cut unit  12  performs a patch cut process on the image read in step S 101 . As illustrated in  FIG. 4 , an image IM read in step S 101  includes an inspection target article T. The image IM is a learning image or an inspection image. When the image IM is a learning image, the inspection target article T included in the image IM is a normal article. When the image IM is an inspection image, the inspection target article T included in the image IM is an article that is to be determined whether it is a normal article or a defect article. 
     In the patch cut process, as illustrated in  FIG. 4 , the patch cut unit  12  cuts out a rectangular image having a preset patch size from the read image IM as a patch image IMp, for example, from the left upper of the image IM. Note that the patch size used for cutting the patch image IMp can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the image IM. 
     Moreover, in the patch cut process, as illustrated in  FIG. 5 , the patch cut unit  12  cuts out and removes a rectangular image having a preset size from the center part of the cut patch image IMp. In such a way, the patch cut unit  12  creates a center-removed patch image IMr, which is a frame-shape patch image IMp for which an image of the center part has been removed, and creates a center image IMc, which is an image cut out of the center part of the patch image IMp. The patch image IMp obtained before the patch cut process is performed includes the center-removed patch image IMr that is a first region and the center image IMc that is a second region and can be considered to be formed of both images. Note that the size at which the center image IMc is cut out can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the patch image IMp. 
     As illustrated in  FIG. 6 , the patch cut unit  12  then cuts out the patch image IMp from the position that has moved in the slide direction in the image IM by a preset slide size in the same manner as described above and creates the center-removed patch image IMr and the center image IMc. Note that the slide size can be set to be equal to or smaller than the width in the slide direction of the patch size for cutting out the patch image IMp. 
     The patch cut unit  12  repeatedly performs the operation illustrated in  FIG. 4  to  FIG. 6  described above for the whole region of the image IM before the patch image IMp is cut out and creates an image pair that is a pair of the center-removed patch image IMr and the center image IMc for the image IM. 
     Note that, while both the patch image IMp and the center image IMc are rectangular in the above description, the shapes thereof are not limited to a quadrilateral such as a rectangular. The shape of the patch image IMp and the center image IMc may be any shape that can be used for creating a pair of the center-removed patch image IMr, which is a peripheral image, and the center image IMc, such as a circle or a triangle, for example, in addition to a quadrilateral. The shape of the patch image IMp and the center image IMc is not required to be the same image or may be different from each other. 
     Further, while an image is cut out of a center part of the patch image IMp in the above description, when a preset size image is cut out of the patch image IMp, a region from which an image is cut out of the patch image IMp is not limited to the center part. That is, the patch cut unit  12  may cut out a part of the patch image IMp and create a partial image that is a part of the patch image IMp instead of the center image IMc. When a partial image that is a part of the patch image IMp is created instead of the center image IMc, the same process is applied except for using the partial image instead of the center image IMc. 
     Next, in step S 103 , the patch cut unit  12  stores an image pair that is a pair of the center-removed patch image IMr and the center image IMc created for the image IM in step S 102  in the patch-processed data storing unit  14 . 
     As described above, the patch cut unit  12  performs the patch cut step for a learning image and an inspection image, respectively. Note that the patch cut step for the learning image is performed before the learning step. On the other hand, the patch cut step for an inspection image can be performed before the learning step or performed after the learning step as long as the patch cut step is performed before the normal image generation step. 
     Next, the normal image learning step in the operation of the image processing apparatus  100  according to the present example embodiment will be described with reference to  FIG. 7  to  FIG. 9 .  FIG. 7  is a flowchart illustrating the normal image learning step in the operation of the image processing apparatus  100  according to the present example embodiment.  FIG. 8  and  FIG. 9  are schematic diagrams illustrating the normal image learning step in the operation of the image processing apparatus  100  according to the present example embodiment. 
     The normal image learning step performed by the normal image learning unit  16  is performed after the patch cut step for the learning image. The normal image learning step performs supervised machine learning by using the learning image data on which the patch cut step has been performed and creates a learning model. 
     First, as illustrated in  FIG. 7 , in step S 201 , the normal image learning unit  16  reads learning data used for the supervised machine learning from the patch-processed data storing unit  14 . Here, the learning data read by the normal image learning unit  16  is data of the image pair that is a pair of a center-removed patch image and a center image created for a learning image. 
     Next, in step S 202 , the normal image learning unit  16  performs a learning process in which learning is performed by using a center-removed patch image as learning data and a center image as training data out of image pairs of the learning data read in step S 201 . The normal image learning unit  16  trains a learning model by using the center-removed patch image and the center image created for the learning image that is an image representing the normal state of an inspection target article. 
       FIG. 8  and  FIG. 9  illustrate a view of learning performed by the normal image learning unit  16  in step S 202 . As illustrated in  FIG. 8 , the normal image learning unit  16  creates a normal image learning model Mn that is a learning model that generates an estimation image used for estimating the center image IMc by restoring the center image IMc that is training data from the center-removed patch image IMr of learning data. Herein, as a scheme for creating the normal image learning model Mn, the normal image learning unit  16  can use a scheme that can reproduce input, such as an autoencoder, for example. Further, the normal image learning unit  16  can perform machine learning using deep learning as a learning scheme, for example. As illustrated in  FIG. 9 , for the image IM that is a learning image, the normal image learning unit  16  performs learning for creating the normal image learning model Mn by using a plurality of image pairs of the center-removed patch image IMr and the center image IMc that are created from the cut patch image IMp. In such a way, the normal image learning model Mn trained by using the center-removed patch image IMr and the center image IMc created for the learning image that is an image representing the normal state of an inspection target article is created. 
     Next, in step S 203 , the normal image learning unit  16  stores the trained normal image learning model created in step S 202  in the normal image learning model storage unit  18 . 
     As described above, the normal image learning unit  16  creates the normal image learning model used for restoring the center image that is the other part of the patch image from the center-removed patch image that is a part of the patch image. The normal image learning unit  16  can create a normal image learning model by performing the learning step before the normal image generation step described later and store the created normal image learning model in the normal image learning model storage unit  18  in advance. 
     In general, in machine learning such as deep learning, a large amount of correct answer data is required to achieve high accuracy. When distinguishing a normal article and a defect article, it is necessary to collect a sufficient amount of data for both normal articles and defect articles as correct answer data. At a site in the actual implementation, however, it is difficult to sufficiently collect data on defect articles. 
     On the other hand, in the present example embodiment, since machine learning is performed by using a center-removed patch image and a center image that are created from the learning image that is an image including a normal article of an inspection target article, an image including a defect article of an inspection target article is not required to be prepared as a learning image. Therefore, according to the present example embodiment, since sufficient amount of learning data can be easily prepared, the learning model can be easily created. 
     Next, the normal image generation step in the operation of the image processing apparatus  100  according to the present example embodiment will be described with reference to  FIG. 10  and  FIG. 11 .  FIG. 10  is a flowchart illustrating the normal image generation step in the operation of the image processing apparatus  100  according to the present example embodiment.  FIG. 11  is a schematic diagram illustrating the normal image generation step in the operation of the image processing apparatus  100  according to the present example embodiment. 
     The normal image generation step performed by the normal image generation unit  22  uses a learning model and a center-removed patch image of a learning image or an inspection image to generate a normal image that is an estimation image used for estimating a center image of a learning image or an inspection image. The normal image generated for the learning image is used for threshold setting. The normal image generated for the inspection image is used for defect detection. 
     First, as illustrated in  FIG. 10 , in step S 301 , the normal image generation unit  22  reads target data used for generating a normal image from the patch-processed data storing unit  14 . Herein, the target data read by the normal image generation unit  22  is the center-removed patch image data created in the patch cut step from the learning image data of the learning data storing unit  10  or the inspection image data of the inspection data storing unit  20 . The normal image generation unit  22  uses the center-removed patch image out of an image pair created for the learning image or the inspection image by the patch cut unit  12  as an image to be input to a learning model. 
     Next, in step S 302 , the normal image generation unit  22  reads the trained learning model from the normal image learning model storage unit  18 . Note that step S 301  and step S 302  may be performed at different times or may be performed at the same time. 
     Next, in step S 303 , the normal image generation unit  22  uses the learning model read in step S 302  to generate a normal image by using the center-removed patch image read in step S 301 . The normal image generation unit  22  uses a learning model to estimate and generate, as a normal image, a center image for the case where an inspection target article is a normal article from the center-removed patch image for the learning image or the inspection image read in step S 301 . 
       FIG. 11  illustrates a view of generation of a normal image for an inspection image performed by the normal image generation unit  22  in step S 303 . As illustrated in  FIG. 11 , the normal image generation unit  22  uses the center-removed patch image IMr created from the cut patch image IMp as an input for the normal image learning model Mn for the image IM that is an inspection image. The normal image generation unit  22  generates, as output of the normal image learning model Mn for input of the center-removed patch image IMr, a normal image IMn that is an image in which the center image IMc for the case where an inspection target article is a normal article is estimated. Note that an inspection target article T included in the image IM that is an inspection image may have a defect D such as a scratch. Also for a learning image, a normal image can be generated in the same manner as in the case of the inspection image illustrated in  FIG. 11 . 
     Next, in step S 304 , the normal image generation unit  22  stores the normal image created in step S 303  in the generated normal data storing unit  24 . 
     As described above, the normal image generation unit  22  uses a learning model to generate a normal image for each of the plurality of center-removed patch images created from the learning image or the inspection image. 
     As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images. 
     In contrast, in the present example embodiment, a normal image is estimated and generated from a center-removed patch image in an inspection image, and a defect article is detected based on the estimated normal image. Thus, in the present example embodiment, detection of a defect article with high robustness can be realized unlike the case where a template of a normal article is used. 
     Furthermore, in the present example embodiment, since a threshold used is set for defect detection as described later, a defect article can be detected at high accuracy even when the image of a normal article has variation. 
     Next, the difference image learning step in the operation of the image processing apparatus  100  according to the present example embodiment will be described with reference to  FIG. 12  and  FIG. 13 .  FIG. 12  is a flowchart illustrating the difference image learning step in the operation of the image processing apparatus  100  according to the present example embodiment.  FIG. 13  is a schematic diagram illustrating the difference image learning step in the operation of the image processing apparatus  100  according to the present example embodiment. 
     The difference image learning step performed by the difference image learning unit  28  is performed after the normal image generation step for a learning image. The difference image learning step creates a learning model by performing supervised machine learning using data of the learning image on which the patch cut step has been performed and data of the difference image generated by the difference calculation unit  26 . 
     First, as illustrated in  FIG. 12 , in step S 401 , the difference calculation unit  26  generates a difference image used for machine learning in the difference image learning unit  28 . In the generation of a difference image, the difference calculation unit  26  calculates the difference between the normal image generated by the normal image generation unit  22  from a center-removed patch image out of an image pair for a learning image and the center image paired with the center-removed patch image to form the image pair. Thereby, the difference calculation unit  26  generates a difference image in which the absolute value of the difference in pixel values for each pixel between the center image and the normal image is the pixel value for the learning image. 
     Next, in step S 402 , the difference image learning unit  28  performs a learning process of performing learning by using the center-removed patch image created for the learning image as the learning data and using the difference image generated in step S 401  as the training data. The difference image learning unit  28  reads the center-removed patch image used as the learning data from the patch-processed data storing unit  14 . The difference image learning unit  28  uses the center-removed patch image created for the learning image and the difference image to train the learning model. 
       FIG. 13  illustrates a view of learning performed by the difference image learning unit  28  in step S 402 . As illustrated in  FIG. 13 , the difference image learning unit  28  creates a difference image learning model Md that is a learning model used for generating an estimation image in which a difference image IMd is estimated by restoring the difference image IMd used as training data from the center-removed patch image IMr of the learning data. Herein, the difference image learning unit  28  can use a scheme that can reproduce input, such as an autoencoder, as a scheme for creating the difference image learning model Md. Further, the difference image learning unit  28  can perform machine learning using deep learning as a learning scheme, for example. The difference image learning unit  28  performs learning for creating the difference image learning model Md by using a plurality of image pairs of the center-removed patch image IMr and the difference image IMd created from the cut patch image IMp for the image IM that is a learning image. In such a way, the difference image learning model Md trained by using the center-removed patch image IMr and the difference image IMd created for the learning image is created. 
     Next, in step S 403 , the difference image learning unit  28  stores the trained difference image learning model created in step S 402  in the difference image learning model storage unit  30 . 
     Next, the defect article detection step in the operation of the image processing apparatus  100  according to the present example embodiment will be described with reference to  FIG. 14  to  FIG. 16 .  FIG. 14  is a flowchart illustrating the defect article detection step in the operation of the image processing apparatus according to the present example embodiment.  FIG. 15  and  FIG. 16  are schematic diagrams illustrating the defect article detection step in the operation of the image processing apparatus  100  according to the present example embodiment. 
     The defect article detection step performed by the defect article detection unit  36  calculates a difference between a center image out of an image pair of a center-removed patch image and a center image created for an inspection image and a normal image generated from the center-removed patch image. Moreover, in the defect article detection step, a defect article, which is abnormal, is distinguished and detected based on the calculated difference. 
     First, as illustrated in  FIG. 14 , in step S 501 , the defect article detection unit  36  reads, as one of the target data, a center image data out of an image pair of a center-removed patch image and a center image created for an inspection image from the patch-processed data storing unit  14 . Further, in step S 501 , the defect article detection unit  36  reads, as the other of the target data, a normal image data created by using a learning model from the center-removed patch image forming an image pair together with the read center image from the generated normal data storing unit  24 . The normal image is a center image estimated from a center-removed patch image. 
     Next, in step S 502 , the defect article detection unit  36  calculates a difference between two types of center images read in step S 501 , that is, a difference between the center image and the normal image. Accordingly, the defect article detection unit  36  generates a difference image in which the absolute value of the difference in the pixel value for each pixel between the center image and the normal image is a pixel value for the inspection image. 
     In such a way, the defect article detection unit  36  and the above difference calculation unit  26  calculate a difference between two types of center images. In such calculation, two types of center images of RGB images or other color images can be used not only for calculation of a difference directly but also for calculation of a difference after performing conversion into another type of images or images defined by another color space and a filtering process, for example. For example, the two types of center images can be used for calculation of a difference after converted into another type of images such as gray scale images or binary images, or images defined by another color space such as HSV or YCbCr. Further, two types of center images can be used for calculation of a difference after a filtering process using a preprocessing filter such as an averaging filter, a median filter, or the like or an edge extraction filter such as a Sobel filter or a Laplacian filter, for example is performed thereon. 
     On the other hand, in step S 503 , the difference image generation unit  32  estimates and generates a difference image from the center-removed patch image created for the inspection image. The difference image generation unit  32  uses the difference image learning model trained in the difference image learning step to estimate the difference image. 
     Next, in step S 504 , the threshold setting unit  34  sets a threshold used for defect detection in the defect article detection unit  36  based on a difference image generated by the difference image generation unit  32  in step S 503 . For example, the threshold setting unit  34  sets each pixel value of a difference image as a threshold for each pixel or sets a value obtained by multiplying each pixel value of a difference image by a constant multiplying factor as a threshold for each pixel. In such a way, in the present example embodiment, a threshold used for defect detection is automatically set by the threshold setting unit  34 . 
     Next, in step S 505 , the defect article detection unit  36  determines on a pixel basis whether or not the difference calculated in step S 502  exceeds the threshold set in step S 504 . 
     If the defect article detection unit  36  determines that the difference does not exceed the threshold (step S 505 , NO), the defect article detection unit  36  determines that the pixel of interest is a normal pixel without an anomaly (step S 507 ). On the other hand, if the defect article detection unit  36  determines that the difference exceeds the threshold (step S 505 , YES), the defect article detection unit  36  determines that the pixel of interest is a defect pixel with an anomaly (step S 506 ). In such a way, the defect article detection unit  36  detects a defect pixel for the center image created from the inspection image. 
     The defect article detection unit  36  determines, based on the defect pixel detected in such a way, whether or not the center image created from the inspection image is normal. For example, the defect article detection unit  36  can determine whether the center image is normal or defective based on the total number of defect pixels, the area of a region in which defect pixels are continuous, or the number of regions including defect pixels. That is, the defect article detection unit  36  can determine that the center image is defective when the area of a region in which defect pixels are continuous or the number of regions including defect pixels exceeds a predetermined threshold. 
       FIG. 15  illustrates a view of a case where the center image IMc created from the image IM that is an inspection image is determined as normal. As illustrated in  FIG. 15 , the defect article detection unit  36  calculates the difference between the center image IMc and the normal image IMn. 
     The difference image generation unit  32  estimates and generates the difference image IMt that is an estimation image by using the difference image learning model Md from the corresponding center-removed patch image IMr. Furthermore, the threshold setting unit  34  sets a threshold based on the difference image IMt generated by the difference image generation unit  32 . 
     The defect article detection unit  36  determines on a pixel basis whether or not the calculated difference between the center image IMc and the normal image IMn exceeds the threshold set by the threshold setting unit  34  and detects a defect pixel. For example, since the total number of defect pixels does not exceed the threshold as a result of the detection of defect pixels in such a way, the defect article detection unit  36  determines that the center image IMc is normal. In the case illustrated in  FIG. 15 , the center image IMc includes no defect D and is thus determined to be normal, because the total number of defect pixel does not exceed the threshold, for example. 
     On the other hand,  FIG. 16  illustrates a view of a case where the center image IMc created from the image IM that is an inspection image is determined as defective. As illustrated in  FIG. 16 , for example, since the total number of defect pixels exceeds the threshold as a result of the detection of defect pixels in the same manner as in the case illustrated in  FIG. 15 , the defect article detection unit  36  determines that the center image IMc is defective. In the case illustrated in  FIG. 16 , the center image IMc includes the defect D and is thus determined to be defective, because the total number of defect pixel exceeds the threshold, for example. 
     Next, in step S 508 , the defect article detection unit  36  outputs a determination result as to whether a center image created for an inspection image is normal or defective. 
     The defect article detection unit  36  performs step S 501  to step S 508  described above on each of the plurality of center images created for an inspection image. 
     Next, in step S 509 , the defect article detection unit  36  detects a defect article and outputs a detection result based on the determination result output in step S 508 . In detection of a defect article, if the number of center images determined to be defective for an inspection image is zero or less than or equal to a predetermined number, the defect article detection unit  36  determines that the inspection target article included in the inspection image is a normal article. On the other hand, if the number of center images determined as defective for the inspection image exceeds the predetermined number, the defect article detection unit  36  determines that the inspection target article included in the inspection image is a defect article. 
     Accordingly, the defect article detection unit  36  determines the quality of an inspection target article as to whether the inspection target article included in the inspection image is a normal article or a defect article to detect a defect article and outputs the detection result. 
     As described above, in the present example embodiment, since a normal image learning model that restores a center image from a center-removed patch image is created by using a learning image including a normal article of an inspection target article, an image including a defect article is not required to be collected. Therefore, according to the present example embodiment, since a sufficient amount of learning data can be easily prepared, a learning model can be easily created. 
     Further, in the present example embodiment, a normal image is estimated and generated from a center-removed patch image in an inspection image to detect a defect article based on the estimated normal image. Thus, according to the present example embodiment, detection of a defect article having high robustness can be realized. 
     Furthermore, in the present example embodiment, a threshold used for detecting a defect pixel is set by the threshold setting unit  34  based on a difference image generated by the difference image generation unit  32  by using a difference image learning model. Accordingly, in the present example embodiment, even when a variable portion that varies on an individual article basis is included in the image of a normal article, a defect article may be detected at high accuracy. 
     The image of a normal article may include a variable portion such as a marking, a label, or the like that varies on an individual article basis. Typically, the variable portion may be, for example, an image portion of a marking, a label, or the like that displays a serial number, a lot number, a date, or the like. When such a variable portion is included, it may be difficult to determine whether a difference between an image of an inspection target article and a corresponding normal image generated by the normal image generation unit  22  is a difference due to the variable portion described above or a difference due to a defect supposed to be detected. Thus, when a difference and a fixed threshold are compared to detect a defect, there is likelihood that a defect may be overlooked if the threshold is set to be larger and a variable portion of a normal article may be erroneously detected as a defect if the threshold is set to be smaller. 
     For example, as illustrated in  FIG. 17 , each image IM including an inspection target article T includes a variable portion that varies on an inspection target article T basis, such as “ABC123”, “ABC124”, . . . . The normal image IMn output by using the normal image learning model Mn by the normal image generation unit  22  from the center-removed patch images IMr of the patch images IMp including such variable portions will be an average-like image in which the variable portions are averaged. Thus, if the threshold is set to be smaller, there is a likelihood that the variable portion of a normal article may be erroneously detected as a defect. On the other hand, if the threshold is set to be larger, there is a likelihood that a defect is overlooked. 
     On the other hand, in the present example embodiment, in learning of the difference image learning model used for setting a threshold, the learning is performed so that a difference that is a reference for a threshold is output in accordance with a range that may be taken by pixel values of a certain pixel in a normal image.  FIG. 18  and  FIG. 19  illustrate distributions of pixel values of a certain pixel in a normal image, respectively. 
     In the case illustrated in  FIG. 18  where there are few variable portions and the range that may be taken by pixel values of a certain pixel in a normal image is narrow, the difference image learning model is trained so that the difference that is a reference for a threshold is smaller. On the other hand, in the case illustrated in  FIG. 19  where there are more variable portions than in the case illustrated in  FIG. 18  and the range that may be taken by pixel values of a certain pixel in a normal image is wide, the difference image learning model is trained so that the difference that is a reference for a threshold is larger than in the case illustrated in  FIG. 18 . 
     In such a way, in the present example embodiment, since a threshold used for defect detection can be set in accordance with a variable portion in a normal image, a defect can be detected at high accuracy. Further, in the present example embodiment, since such a threshold can be automatically set by the threshold setting unit  34 , the labor for manually setting a threshold can be reduced. 
     As described above, according to the present example embodiment, since an estimation image including at least a predetermined region of an inspection target article is generated as a normal image by using a part of an inspection image including an inspection target article, it is possible to distinguish a defect article, which is abnormal, at high accuracy while reducing influence of an individual difference of images. 
     Note that, as illustrated in  FIG. 20 , the image processing apparatus  100  according to the present example embodiment can also be formed to have a detection result storing unit  38  and a detection result display unit  40 .  FIG. 20  is a block diagram illustrating a function configuration of an image processing apparatus according to a modified example of the present example embodiment. 
     In the case illustrated in  FIG. 20 , the defect article detection unit  36  stores a detection result of a defect article in the detection result storing unit  38 . The defect article detection unit  36  can storing, in the detection result storing unit  38 , information indicating whether or not an inspection target article is a normal article or a defect article, data of an image indicating a defect position if the inspection target article is a defect article, or the like together with an identifier that identifies an inspection image. Further, the defect article detection unit  36  can also store a detection result of a defect pixel in the detection result storing unit  38 . 
     The detection result storing unit  38  stores a detection result of a defect article or a defect pixel output by the defect article detection unit  36 . For example, a database storing a detection result is stored in the detection result storing unit  38 . A function of the detection result storing unit  38  is implemented by the HDD  1008  as with the learning data storing unit  10  or the like. 
     The detection result display unit  40  displays a detection result of a defect article or a defect pixel stored in the detection result storing unit  38 . When displaying a detection result, the detection result display unit  40  can display an image representing a defect position by displaying the defect pixel. The detection result display unit  40  is implemented by the output device  1010  as a display device. 
     Further, in the case illustrated in above  FIG. 8 , the normal image learning unit  16  trains and creates a learning model that restores a center image IMc of a center region forming a patch image IMp from a center-removed patch image IMr of a frame-shape region forming a patch image IMp. However, the example embodiment is not limited thereto. For example, the normal image learning unit  16  can also train and create a learning model that restores the whole patch image IMp including the center image IMc from the center-removed patch image IMr to generate an estimation image in which the patch image IMp is estimated. In such a case, the normal image generation unit  22  can use such a learning model and generate, as a normal image, an estimation image used for estimating the whole of the patch image IMp in the case of a normal article. Further, the defect article detection unit  36  can compare the whole patch image IMp for an inspection image with an estimation image in which the whole patch image IMp is estimated and detect a defect article. 
     Other Example Embodiments 
     The image processing apparatus described in each of the above example embodiments can also be formed as illustrated in  FIG. 21  according to another example embodiment.  FIG. 21  is a block diagram illustrating a function configuration of an image processing apparatus according to another example embodiment 
     As illustrated in  FIG. 21 , an image processing apparatus  2000  has a first generation unit  2002  that generates a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including an inspection target. Further, the image processing apparatus  2000  has a second generation unit  2004  that estimates a difference between the first estimation image and the inspection image to generate a second estimation image by using a part of the inspection image. Further, the image processing apparatus  2000  has a comparison unit  2006  that compares the first estimation image with the inspection image and an output unit  2008  that outputs a comparison result obtained by the comparison unit  2006 . The comparison unit  2006  compares the difference between the first estimation image and the inspection image with the second estimation image. 
     According to the image processing apparatus  2000  according to another example embodiment, since an estimation image including at least a predetermined region of an inspection target is generated by using a part of an inspection image including an inspection target, it is possible to distinguish an anomaly at high accuracy while reducing influence of an individual difference of images. 
     Further, according to yet another example embodiment, the image processing apparatus described in each of the above example embodiments can be formed as illustrated in  FIG. 22 .  FIG. 22  is a block diagram illustrating a function configuration of an image processing apparatus according to yet another example embodiment. 
     As illustrated in  FIG. 22 , an image processing apparatus  3000  according to yet another example embodiment has a first generation unit  3002  that generates a second image including at least a predetermined region of an object by using a part of a first image including an object. Further, the image processing apparatus  3000  has a second generation unit  3004  that estimates a difference between the second image and the first image to generate a third image by using a part of the first image. Further, the image processing apparatus  3000  has a comparison unit  3006  that compares the second image with the first image and output unit  3008  that outputs a comparison result obtained by the comparison unit  3006 . The comparison unit  3006  compares the difference between the second image and the first image with the third image. 
     According to the image processing apparatus  3000  according to yet another example embodiment, since a second image including at least a predetermined region of an object is generated by using a part of a first image including an object, it is possible to distinguish an anomaly at high accuracy while reducing influence of an individual difference of images. 
     Modified Example Embodiments 
     The present invention is not limited to the example embodiments described above, and various modifications are possible. 
     For example, while the case of inspection to detect a defect article from an inspection target article has been described as an example in the above example embodiments, the example embodiment is not limited thereto. The present invention can be widely applied to the case of determining whether an object is in a normal state or an abnormal state to detect an abnormal state of the object, that is, a state other than the normal state. The present invention can also be applied to a case of detecting breakage of an object such as a building as an anomaly, a case of detecting an abnormal object, or the like, for example. 
     Further, the scope of each of the example embodiments further includes a processing method that stores, in a storage medium, a program that causes the configuration of each of the example embodiments to operate so as to implement the function of each of the example embodiments described above, reads the program stored in the storage medium as a code, and executes the program in a computer. That is, the scope of each of the example embodiments also includes a computer readable storage medium. Further, each of the example embodiments includes not only the storage medium in which the computer program described above is stored but also the computer program itself. 
     As the storage medium, for example, a floppy (registered trademark) disk, a hard disk, an optical disk, a magneto-optical disk, a compact disc-read only memory (CD-ROM), a magnetic tape, a nonvolatile memory card, or a ROM can be used. Further, the scope of each of the example embodiments includes an example that operates on operating system (OS) to perform a process in cooperation with another software or a function of an add-in board without being limited to an example that performs a process by an individual program stored in the storage medium. 
     A service implemented by the function of each of the example embodiments described above may be provided to a user in a form of Software as a Service (SaaS). 
     The whole or part of the example embodiments disclosed above can be described as, but not limited to, the following supplementary notes. 
     (Supplementary Note 1)
         An image processing apparatus comprising:   a first generation unit that generates a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target;   a second generation unit that estimates a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image;   a comparison unit that compares the first estimation image with the inspection image; and   an output unit that outputs a comparison result obtained by the comparison unit,   wherein the comparison unit compares a difference between the first estimation image and the inspection image with the second estimation image.       

     (Supplementary Note 2)
         The image processing apparatus according to supplementary note 1,   wherein the inspection image includes a first region and a second region,   wherein the first generation unit generates the first estimation image of the second region by using an image of the first region in the inspection image,   wherein the second generation unit estimates a difference between the first estimation image of the second region and the image of the second region in the inspection image to generate the second estimation image by using the image of the first region in the inspection image, and   wherein the comparison unit compares a difference between the first estimation image of the second region and the image of the second region in the inspection image with the second estimation image.       

     (Supplementary Note 3)
         The image processing apparatus according to supplementary note 2, wherein the first generation unit generates the first estimation image by using a first learning model trained so as to estimate the image of the second region from the image of the first region.       

     (Supplementary Note 4)
         The image processing apparatus according to supplementary note 3, wherein the first learning model is trained by using an image representing a normal state of the inspection target.       

     (Supplementary Note 5)
         The image processing apparatus according to any one of supplementary notes 2 to 4, wherein the second generation unit generates the second estimation image by using a second learning model trained so as to estimate a difference between the first estimation image of the second region and the image of the second region from the image of the first region.       

     (Supplementary Note 6)
         The image processing apparatus according to supplementary note 5, wherein the second learning model is trained by using a difference image between the first estimation image of the second region and an image of the second region for an image representing a normal state of the inspection target including the first region and the second region.       

     (Supplementary Note 7)
         The image processing apparatus according to any one of supplementary notes 1 to 6 further comprising a threshold setting unit that sets a threshold based on the second estimation image generated by the second generation unit,   wherein the comparison unit detects a defect in the inspection image by comparing a difference between the first estimation image and the inspection image with the threshold set by the threshold setting unit.       

     (Supplementary Note 8)
         The image processing apparatus according to supplementary note 7,   wherein the threshold setting unit sets the threshold on a pixel basis, and   wherein the comparison unit detects a defect pixel in the inspection image by comparing a difference between the first estimation image and the inspection image with the threshold on a pixel basis.       

     (Supplementary Note 9)
         The image processing apparatus according to any one of supplementary notes 1 to 8, wherein the first estimation image includes an image representing a normal state of the inspection target in the at least predetermined region.       

     (Supplementary Note 10)
         The image processing apparatus according to any one of supplementary notes 1 to 9 further comprising a determination unit that determines quality of the inspection target based on the comparison result obtained by the comparison unit.       

     (Supplementary Note 11)
         An image processing method comprising:   a first generation step of generating a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target;   a second generation step of estimating a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image;   a comparison step of comparing the first estimation image with the inspection image; and   an output step of outputting a comparison result obtained by the comparison step,   wherein the comparison step compares a difference between the first estimation image and the inspection image with the second estimation image.       

     (Supplementary Note 12)
         A storage medium storing a program that causes a computer to perform an image processing method comprising:   a first generation step of generating a first estimation image including at least a predetermined region of an inspection target by using a part of an inspection image including the inspection target;   a second generation step of estimating a difference between the first estimation image and the inspection image to generate a second estimation image by using the part of the inspection image;   a comparison step of comparing the first estimation image with the inspection image; and   an output step of outputting a comparison result obtained by the comparison step,   wherein the comparison step compares a difference between the first estimation image and the inspection image with the second estimation image.       

     (Supplementary Note 13)
         An image processing apparatus comprising:   a first generation unit that generates a second estimation image including at least a predetermined region of an object by using a part of a first image including the object;   a second generation unit that estimates a difference between the second image and the first image to generate a third image by using the part of the first image;   a comparison unit that compares the second image with the first image; and   an output unit that outputs a comparison result obtained by the comparison unit,   wherein the comparison unit compares a difference between the second image and the first image with the third image.       

     As described above, while the present invention has been described with reference to the example embodiments, the present invention is not limited to these example embodiments described above. Various modifications that can be appreciated by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2018-012307, filed on Jan. 29, 2018, the disclosure of which is incorporated herein in its entirety by reference. 
     REFERENCE SIGNS LIST 
     
         
           10  learning data storing unit 
           12  patch cut unit 
           14  patch-processed data storing unit 
           16  normal image learning unit 
           18  normal image learning model storage unit 
           20  inspection data storing unit 
           22  normal image generation unit 
           24  generated normal data storing unit 
           26  difference calculation unit 
           28  difference image learning unit 
           30  difference image learning model storage unit 
           32  difference image generation unit 
           34  threshold setting unit 
           36  defect article detection unit 
           38  detection result storing unit 
           40  detection result display unit 
           100  image processing apparatus