Patent Publication Number: US-11030723-B2

Title: Image processing apparatus, image processing method, and program

Description:
CROSS REFERENCE TO PRIOR APPLICATION 
     This application is a National Stage Patent Application of PCT International Patent Application No. PCT/JP2017/043311 (filed on Dec. 1, 2017) under 35 U.S.C. § 371, which claims priority to Japanese Patent Application No. 2017-029353 (filed on Feb. 20, 2017), which are all hereby incorporated by reference in their entirety. 
     TECHNICAL FIELD 
     The present disclosure relates to an image processing apparatus, an image processing method, and a program. In particularly, the present disclosure relates to an image processing apparatus, an image processing method, and a program that perform image processing for improving image quality. 
     BACKGROUND ART 
     In recent years, a system has been widely used in which a visible camera that captures a visible image and a far-infrared camera that can capture, for example, an image of a person even at night when it is difficult to capture images with visible light are combined. 
     An imaging apparatus using two cameras is disclosed in, for example, Patent Literature 1 (JP-A-2011-211387). 
     However, in general, an infrared image captured by an infrared camera has a problem that the resolution thereof is lower than the resolution of a visible image. 
     In addition, a visible image which is a general color image is also used in an endoscope that captures the image of the inside of a living body. In recent years, a fluorescent image different from the visible image has been used. 
     The fluorescent image is, for example, an image obtained by emitting excitation light in a specific wavelength range and capturing fluorescence included in light reflected from a substance in the living body. 
     The fluorescent image can express, for example, a difference in intensity corresponding to a lesion in the living body and the use of the fluorescent image makes it possible to effectively analyze, for example, the progress of a disease. 
     It should be noted that an endoscopic apparatus using a visible image and a fluorescent image is disclosed in, for example, Patent Literature 2 (JP-A-2013-248319). 
     However, similarly to the above-mentioned infrared image, the fluorescent image also has the disadvantage that only an image having a lower resolution than the visible image is obtained and image quality is reduced. In particular, for an image of, for example, a blood vessel at a deep position in the living body, a clear image is not obtained since a larger amount of scattered light is generated in the living body. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP-A-2011-211387 
     Patent Literature 2: JP-A-2013-248319 
     DISCLOSURE OF INVENTION 
     Technical Problem 
     The present disclosure has been made in view of, for example, the above-mentioned problems and an object of the present disclosure is to provide an image processing apparatus, an image processing method, and a program that improve the quality of a low-quality image, such as a far-infrared image or a fluorescent image, using image processing to generate a high-quality image. 
     Solution to Problem 
     In accordance with a first aspect of the present disclosure, there is provided an image processing apparatus including an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units. The image correction unit inputs a low-quality image which is an image to be corrected and a high-quality image which is a reference image. Each of the plurality of processing units in each stage performs a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image. The class correspondence correction coefficient is generated by a learning process. 
     In addition, according to a second aspect of the present disclosure, there is provided an image processing method to be performed in an image processing apparatus including an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units. The image processing method includes: an image input step of allowing the image correction unit to input a low-quality image which is an image to be corrected and a high-quality image which is a reference image; and a correction step of allowing each of the plurality of processing units in each stage to perform a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image. The class correspondence correction coefficient used in the correction step is generated by a learning process. 
     Further, according to a third aspect of the present disclosure, there is provided a program that causes an image processing apparatus including an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units to perform image processing. The program causes the image correction unit to perform an image input step of inputting a low-quality image which is an image to be corrected and a high-quality image which is a reference image and causes each of the plurality of processing units in each stage to perform a correction step of performing a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image. The class correspondence correction coefficient used in the correction step is generated by a learning process. 
     It should be noted that, for example, the program according to the present disclosure can be provided by a storage medium or a communication medium which is provided in a computer-readable form to an information processing apparatus or a computer system capable of executing various program codes. Since the program is provided in a computer readable form, processes corresponding to the program are implemented in the information processing apparatus or the computer system. 
     Other objects, features, and advantages of the present disclosure will become apparent from the more detailed description based on the embodiments of the present disclosure which will be described below and the accompanying drawings. It should be noted that, in the specification, a system is a logical set configuration of a plurality of apparatuses and is not limited to the configuration in which the apparatuses are provided in the same housing. 
     Advantageous Effects of Invention 
     In accordance with the configuration of an embodiment of the present disclosure, an apparatus and a method that perform a process of improving the quality of a low-quality image, such as a far-infrared image, are achieved. 
     Specifically, for example, the apparatus includes an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages. The image correction unit inputs a low-quality image which is an image to be corrected and a high-quality image which is a reference image. Each of the processing units in each stage performs a correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from a degraded image of the high-quality image. A processing unit in a previous stage performs the correction process, using a class correspondence correction coefficient corresponding to a feature amount extracted from an image having a higher degradation level than that in a processing unit in a subsequent stage. The class correspondence correction coefficient is generated by a learning process. 
     An apparatus and a method that perform a process of improving the quality of a low-quality image, such as a far-infrared image, are achieved by these processes. 
     It should be noted that the effects described in the specification are just illustrative and are not limited and additional effects may be obtained. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating a fluorescent image. 
         FIG. 2  is a diagram illustrating a correspondence relationship between the type of captured image and the wavelength of light. 
         FIG. 3  is a diagram illustrating an example of the arrangement of pixels in a visible image and an infrared image. 
         FIG. 4  is a diagram illustrating a process performed by an image processing apparatus according to the present disclosure. 
         FIG. 5  is a diagram illustrating the configuration and process of an image processing unit. 
         FIG. 6  is a diagram illustrating the configuration and process of an image correction unit. 
         FIG. 7  is a diagram illustrating the execution configuration and process of a learning process of acquiring a feature amount from an image and calculating a correction coefficient. 
         FIG. 8  is a diagram illustrating the execution configuration and process of the learning process of acquiring a feature amount from an image and calculating a correction coefficient. 
         FIG. 9  is a diagram illustrating a feature amount acquired from an image, a correction coefficient, and a class classification process. 
         FIG. 10  is a diagram illustrating an example of a process of acquiring a feature amount from an image and performing a correction process using a correction coefficient subjected to class classification. 
         FIG. 11  is a diagram illustrating an example of the process of acquiring a feature amount from an image and performing a correction process using a correction coefficient subjected to class classification. 
         FIG. 12  is a diagram illustrating an example of the process of acquiring a feature amount from an image and performing a correction process using a correction coefficient subjected to class classification. 
         FIG. 13  is a diagram illustrating the multi-stage configuration and process of the image correction unit. 
         FIG. 14  is a diagram illustrating the type and specific example of the feature amount of an image. 
         FIG. 15  is a diagram illustrating an example of the multi-stage configuration of the image correction unit. 
         FIG. 16  is a flowchart illustrating the sequence of the learning process performed by the image processing apparatus. 
         FIG. 17  is a flowchart illustrating the sequence of an image correction process performed by the image processing apparatus. 
         FIG. 18  is a diagram illustrating an example of the hardware configuration of the image processing apparatus. 
         FIG. 19  is a block diagram illustrating an example of the schematic configuration of a vehicle control system. 
         FIG. 20  is a diagram illustrating an example of the installation position of an out-of-vehicle information detection unit and an imaging unit. 
     
    
    
     MODE(S) FOR CARRYING OUT THE INVENTION 
     Hereinafter, an image processing apparatus, an image processing method, and a program according to the present disclosure will be described in detail with reference to the drawings. It should be noted that the description will be made in accordance with the following items: 
     1. For Configuration and Process of Image Processing Apparatus According to Present Disclosure 
     2. For Example of Configuration of Image Processing Apparatus Performing Process of Improving Quality of Low-quality Image 
     3. For Example of Configuration and Process of Image Correction Unit 
     4. For Calculation of Class Correspondence Correction Coefficient by Learning Process and Storage of Class Correspondence Correction Coefficient in Storage Unit 
     5. For Image Correction Process Using Class Correspondence Correction Coefficient Calculated by Learning Process 
     6. For Other Embodiments 
     6-1. For Example of Feature Amounts Usable For Image Correction Process 
     6-2. For Other Examples of Multi-stage Configuration of Image Correction Unit 
     7. For Sequence of Process Performed by Image Processing Apparatus 
     8. For Example of Hardware Configuration of Image Processing Apparatus 
     9. For Application Examples of Image Processing Apparatus According to Present Disclosure 
     10. Summary of Configuration of Present Disclosure 
     1. FOR CONFIGURATION AND PROCESS OF IMAGE PROCESSING APPARATUS ACCORDING TO PRESENT DISCLOSURE 
     The configuration and process of an image processing apparatus according to the present disclosure will be described with reference to  FIG. 1  and the subsequent figures. 
     First, an image to be processed by the image processing apparatus according to the present disclosure will be described with reference to  FIG. 1  and the subsequent figures. 
     The image processing apparatus according to the present disclosure performs a quality improvement process of performing image processing for a low-quality image, such as a far-infrared image or a fluorescent image having a lower resolution than a general visible image, to improve the quality of the image. 
     Specifically, the image processing apparatus performs a quality improvement process of performing image processing for a combination of a low-quality image, such as a far-infrared image or a fluorescent image, and a visible image which is a high-quality image having the same object as the low-quality image to improve the quality of the far-infrared image or the fluorescent image. 
     Hereinafter, the fluorescent image and the far-infrared image which are low-quality images to be subjected to the quality improvement process will be described. 
     First, the fluorescent image will be described. 
     As described above, the use of a fluorescent image different from a visible image in addition to the visible image which is a general color image is increasing in an endoscope that captures the image of the inside of a living body. 
     The fluorescent image is an image obtained by emitting excitation light with a specific wavelength and capturing fluorescence included in light reflected from a substance in the living body. 
     The fluorescent image can express, for example, a difference in intensity according to a lesion in the living body and the use of the fluorescent image makes it possible to effectively analyze the progress of a disease. 
     An example of the configuration of capturing the fluorescent image will be described with reference to  FIG. 1 . 
     The fluorescent image is an image obtained by emitting excitation light with a specific wavelength, inputting fluorescence which has been output from a living body tissue, such as a blood vessel, to an imaging element, and capturing the fluorescence. 
       FIG. 1 ( 1 ) illustrates an example of the configuration of capturing an image of a blood vessel  11  in a relatively shallow portion of a living body tissue  10  and  FIG. 1 ( 2 ) illustrates an example of the configuration of capturing an image of the blood vessel  11  in a relatively deep portion of the living body tissue  10 . 
     In a case in which the blood vessel is irradiated with excitation light, a plurality of scattered light components are generated. In particular, in the deep portion of the living body tissue  10 , a larger amount of scattered light is generated. As a result, the quality of the fluorescent image captured by the imaging element is degraded. 
     Next, a far-infrared image will be described with reference to  FIG. 2 . 
     As illustrated in  FIG. 2 , a visible image  20  is an image in a wavelength range of about 0.4 μm to 0.7 μm and is a color image such as an RGB image captured by a general camera. 
     In contrast, the far-infrared image is an image formed by long-wavelength light with a wavelength of 0.7 μm or more. An infrared imaging camera that captures infrared images can capture, for example, an image of a person that generates heat in the dark and is used as, for example, a surveillance camera. 
     It should be noted that infrared rays are divided into near-infrared rays with a wavelength of about 0.7 μm to 1 μm, mid-infrared rays with a wavelength of about 3 μm to 5 μm, and far-infrared rays with a wavelength of about 8 μm to 14 μm as illustrated in  FIG. 2 . 
     Among these images, a far-infrared image  21  obtained by mainly capturing far-infrared rays with a wavelength of about 8 μm to 14 μm is an image based on far-infrared rays with a longer wavelength and there is a problem that the resolution of the far-infrared image  21  is significantly reduced. 
       FIG. 3  is a diagram illustrating an example of the arrangement of pixels on an imaging element that captures the visible image  20  and the far-infrared image  21 . 
     The visible image illustrated in  FIG. 3 ( 1 ) shows an example of a Bayer array of R, G, and B pixels. The Bayer array is used for imaging elements of many visible imaging cameras. 
     Each pixel of the imaging element outputs an electric signal corresponding to the amount of light with R, G, or B wavelengths. 
     In contrast, the far-infrared image illustrated in  FIG. 3 ( 2 ) is obtained by capturing light with a far-infrared (FIR) wavelength at all pixel positions. 
     However, as illustrated in  FIG. 3 ( 1 ) and  FIG. 3 ( 2 ), in general, a far-infrared imaging element has a lower resolution than a visible imaging element. The reason is that infrared rays, particularly, far-infrared rays have a long wavelength and it is difficult for an imaging element having a high-density pixel array to use the infrared rays. 
     Similarly to the far-infrared image, for the fluorescent image, it is difficult to acquire a high-resolution image. 
     The image processing apparatus according to the present disclosure generates a high-quality image, using an image correction process of improving the quality of a low-quality image such as the far-infrared image or the fluorescent image. 
     Specifically, the image processing apparatus performs a quality improvement process of performing image processing for a combination of a low-quality image, such as a far-infrared image or a fluorescent image, and a visible image which is a high-quality image having the same object as the low-quality image to improve the quality of the low-quality image such as the far-infrared image or the fluorescent image. 
     It should be noted that the quality improvement process according to the present disclosure can be applied not only to the far-infrared image or the fluorescent image, but also to other low-quality images. 
     2. FOR EXAMPLE OF CONFIGURATION OF IMAGE PROCESSING APPARATUS PERFORMING PROCESS OF IMPROVING QUALITY OF LOW-QUALITY IMAGE 
       FIG. 4  is a block diagram illustrating the configuration of an imaging apparatus which is an example of an image processing apparatus  100  according to the present disclosure. 
     It should be noted that the image processing apparatus according to the present disclosure is not limited to the imaging apparatus and includes, for example, an information processing apparatus such as a PC that receives an image captured by the imaging apparatus and performs image processing. 
     Hereinafter, the configuration and process of the imaging apparatus as an example of the image processing apparatus  100  according to the present disclosure will be described. 
     Image processing other than an imaging process described in the following embodiments can be performed not only by the imaging apparatus, but also by the information processing apparatus such as a PC. 
     The image processing apparatus  100  as the imaging apparatus illustrated in  FIG. 4  includes a control unit  101 , a storage unit  102 , a codec  103 , an input unit  104 , an output unit  105 , an imaging unit  106 , and an image processing unit  120 . 
     The imaging unit  106  includes a high-quality imaging unit  107  that captures a high-quality image with high resolution, such as a general visible image, and a low-quality imaging unit  108  that captures a low-quality image with low resolution, such as a far-infrared image or a fluorescent image. 
     The high-quality imaging unit  107  includes a first imaging element  111  that captures, for example, a visible image with high resolution. The first imaging element  111  includes, for example, R, G and B pixels that are arranged in the Bayer array described with reference to  FIG. 2 ( 1 ) and each pixel outputs a signal corresponding to input light of each of R, G, and B. 
     In contrast, the low-quality imaging unit  108  includes a second imaging element  112  that captures a low-quality image with low resolution such as a far-infrared image or a fluorescent image. The second imaging element  112  captures, for example, a far-infrared image or a fluorescent image and can capture only an image with a lower quality than the first imaging element  111 . 
     A high-quality image  151  which is an image captured by the high-quality imaging unit  107  and a low-quality image  152  which is an image captured by the low-quality imaging unit  108  are input to the image processing unit  200 . 
     The image processing unit  200  performs a process of improving the quality of the low-quality image  152 , that is, a quality improvement process, using the two images. 
     The image processing unit  200  generates a high-quality corrected image  172  and outputs the high-quality corrected image  172  as the processing result. 
     The high-quality corrected image  172  is a high-quality far-infrared image or a high-quality fluorescent image generated by performing a correction process for the low-quality image such as the far-infrared image or the fluorescent image captured by the low-quality imaging unit  108 . 
     The high-quality imaging unit  107  and the low-quality imaging unit  108  are two imaging units set at positions that are a predetermined distance away from each other and capture images from different viewpoints. 
     The same object image is not captured by the corresponding pixels, that is, the pixels at the same position in two images captured from different viewpoints and object deviation corresponding to disparity occurs. 
     In a case in which the captured images are still images, each of the high-quality imaging unit  107  and the low-quality imaging unit  108  captures one still image. That is, a total of two still images are captured. In a case in which a moving image is captured, each of the imaging units captures continuous image frames. 
     It should be noted that the control unit  101  controls the imaging timing of the imaging units. 
     The control unit  101  controls various processes of the imaging apparatus  100 , such as an imaging process, signal processing for a captured image, an image recording process, and a display process. The control unit  101  includes, for example, a CPU that performs processes according to various processing programs stored in the storage unit  102  and functions as a data processing unit that executes programs. 
     The storage unit  102  is, for example, a RAM or a ROM that functions as a captured image storage unit, a storage unit storing processing programs executed by the control unit  101  or various parameters, and a work area at the time of data processing. 
     The codec  103  performs a coding and decoding process such as a process of compressing and decompressing a captured image. 
     The input unit  104  is, for example, a user operation unit and is used to input control information such as information related to the start and end of imaging and the setting of various modes. 
     For example, the output unit  105  includes a display unit and a speaker and is used to display captured images and through images and to output voice. 
     The image processing unit  120  receives two images input from the imaging unit  106  and performs a process of improving the quality of the input images using the two images. 
     Specifically, the image processing unit  120  performs the process of improving the quality of the low-quality image  152 , such as a far-infrared image or a fluorescent image, captured by the low-quality imaging unit  108  to generate the high-quality corrected image  172  and outputs the high-quality corrected image  172 . 
     As described above, the high-quality corrected image  172  is a high-quality far-infrared image or a high-quality fluorescent image generated by a correction process for a low-quality image, such as a far-infrared image or a fluorescent image, captured by the low-quality imaging unit  108 . 
     The configuration and process of the image processing unit  120  will be described with reference to  FIG. 5  and the subsequent figures. 
     In this embodiment, the image processing unit  120  receives two types of images, that is, the high-quality image  151  captured by the high-quality imaging unit  107  and the low-quality image  152  captured by the low-quality imaging unit  108  and performs the process of improving the quality of the low-quality image  152  with the two types of images to generate the high-quality corrected image  172 . 
     The process performed by the image processing unit  120  will be described. 
     In the image processing unit  120 , the low-quality image  152  captured by the low-quality imaging unit  108  is input to a scaler  121  and the scaler  121  performs a scaling process of making the size of the low-quality image  152  equal to the size of the high-quality image  151 . 
     This is an image size adjustment process for removing the difference between the size of the first imaging element  111  of the low-quality imaging unit  108  and the size of the second imaging element of the high-quality imaging unit  107 . 
     In many cases, the size of the first imaging element  111  of the low-quality imaging unit  108  is less than the size of the second imaging element of the high-quality imaging unit  107 . 
     The scaler  121  performs a scaling process of making the size of the low-quality image  152  equal to the size of the high-quality image  151 . 
     The high-quality image  151  and the low-quality image  152  having the same size are input to a disparity amount and movement detection unit  122  and an image positioning unit  123 . 
     The disparity amount and movement detection unit  122  detects the amount of disparity between the high-quality image  151  and the low-quality image  152  and the amount of movement between the two images. 
     The low-quality imaging unit  108  and the high-quality imaging unit  107  are two imaging units set at the positions that are a predetermined distance away from each other and capture images (the high-quality image  151  and the low-quality image  152 ) from different viewpoints. 
     The same object image is not captured by the corresponding pixels, that is, the pixels at the same position in two images captured from different viewpoints, that is, the high-quality image  151  and the low-quality image  152  and object deviation corresponding to disparity occurs. 
     In addition, the two images are not captured at the exactly same timing. In a case in which the objects include a moving object, the positions of the same object in the two images are different from each other. That is, the amount of movement of the object exists. 
     The disparity amount and movement detection unit  122  detects the amount of disparity between the high-quality image  151  and the low-quality image  152  and the amount of movement between the two images and inputs information thereof, that is, disparity information and movement information, for example, a motion vector (MV) to the image positioning unit  123 . 
     The image positioning unit  123  performs a positioning process for the high-quality image  151  and the low-quality image  152  subjected to size adjustment, using the disparity information and the movement information input from the disparity amount and movement detection unit  122 . 
     That is, the image positioning unit  123  performs the positioning process for the two images such that the same object is located at the same position of each image. 
     It should be noted that, specifically, the image positioning unit  123  performs a positioning process of setting, for example, the high-quality image  151  to a reference position and aligning an object position of the low-quality image  152  with an object position of the high-quality image  151 , without moving the object position of the high-quality image  151 . 
     However, the image to be used as the reference image is not particularly limited and any image may be used as the reference image. 
     The image positioning unit  123  outputs the positioned two images, that is, a positioned high-quality image  161  and a positioned low-quality image  162  illustrated in  FIG. 5  to an image correction unit  127 . 
     The image correction unit  127  receives the positioned high-quality image  161  and the positioned low-quality image  162  and performs a process of improving the quality of the positioned low-quality image  162 . 
     3. FOR EXAMPLE OF CONFIGURATION AND PROCESS OF IMAGE CORRECTION UNIT 
     Next, a specific example of the configuration and process of the image correction unit  127  in the image processing unit  120  illustrated in  FIG. 5  will be described with reference to  FIG. 6 . 
     The image correction unit  127  illustrated in  FIG. 6  has a multi-stage (cascade) configuration having the following three-stage processing units: 
     A first-stage processing unit  210 ; 
     A second-stage processing unit  220 ; and 
     A third-stage processing unit  230 . 
     The three processing units have the same components and include the following elements: 
     Degradation-simulated image generation units  211 ,  221 , and  231 ; 
     Class classification processing units  212 ,  222 , and  232 ; 
     Class correspondence correction coefficient storage units  213 ,  223 , and  233 ; 
     Tap selection units  214 ,  224 , and  234 ; and 
     Image correction units  215 ,  225 , and  235 . 
     First, the outline of each of the components will be described and a specific process of each component will be described in detail below. 
     The positioned high-quality image  161  is input to the degradation-simulated image generation units  211 ,  221 , and  231  and the degradation-simulated image generation units  211 ,  221 , and  231  generate simulated images with different degradation levels. 
     For example, the degradation-simulated image generation units  211 ,  221 , and  231  a perform pixel value conversion process using a plurality of different low-pass filters (LPFs) for the positioned high-quality image  161  to generate the simulated images with different degradation levels. 
     The class classification processing units  212 ,  222 , and  232  perform a class classification process for each pixel region of the degradation-level simulated images with each degradation level on the basis of the feature amount of each predetermined pixel region (each local region) of the degradation-simulated images. 
     The class classification process is a process of classifying classes for determining correction coefficients (correction parameters) used in the correction process performed by the image correction units  215 ,  225 , and  235  in each stage. 
     It should be noted that the correction coefficients (correction parameters) corresponding to each class are stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233  in advance. 
     For example, the correction coefficients (correction parameters) calculated by a learning process using a sample image are stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233  in advance so as to be associated with each class. This learning process will be described below. 
     The correction coefficients (correction parameters) corresponding to the class decided by the class classification processing units  212 ,  222 , and  232 , that is, the class corresponding to a predetermined pixel region of each of the degraded images generated by the degraded image generation units  212 ,  222 , and  232  are output from the class correspondence correction coefficient storage units  213 ,  223 , and  233  to the image correction units  215 ,  225 , and  235 . The image correction units  215 ,  225 , and  235  correct the pixel value of the positioned low-quality image  162 , using the class correspondence correction coefficients. 
     It should be noted that, first, the positioned low-quality image  162  is input to the tap selection units  214 ,  224 , and  234  before it is corrected by the image correction units  215 ,  225 , and  235  and the tap selection units  214 ,  224 , and  234  perform a tap selection process. Specifically, a process is performed which determines the position of a reference pixel applied to decide a correction pixel value of a pixel to be corrected, that is, a reference pixel in the vicinity of the position of the pixel to be corrected. 
     The image correction units  215 ,  225 , and  235  decide the value of each pixel of the positioned low-quality image  162 , using the pixel values of the taps (reference pixels) decided by the tap selection units  214 ,  224 , and  234  and the class correspondence correction coefficients input from the class correspondence correction coefficient storage units  213 ,  223 , and  233 . 
     As illustrated in  FIG. 6 , in the configuration according to the present disclosure, the correction of this pixel value is performed as a multi-stage process (cascade process). 
     In this case, the correction coefficient applied in the processing unit in each stage is a correction coefficient subjected to class classification in the learning process performed in advance and the correction coefficient can be set as an accurate correction coefficient corresponding to a larger number of classes. 
     For example, in the example illustrated in  FIG. 6 , three-stage correction processing units in the first to third stages are provided. In a case in which the number of classes associated with the correction coefficients in each stage is 1000 classes, in three-stage correction processes in the first to third stages, correction can be performed using different correction coefficients corresponding to 1000×1000×1000=1000000000, that is, 1K×1K×1K=1G classes. As a result, accurate correction corresponding to finely classified image characteristics is achieved. 
     The image processing apparatus according to the present disclosure achieves optimal correction corresponding to the feature amount of each local region of the image using the above-mentioned process. 
     [4. For Calculation of Class Correspondence Correction Coefficient by Learning Process and Storage of Class Correspondence Correction Coefficient in Storage Unit] 
     Next, the calculation of the class correspondence correction coefficient by the learning process and the storage of the class correspondence correction coefficient in the storage unit will be described. 
     A class correspondence correction coefficient calculation process and a process of storing the calculated correction coefficients in the class correspondence correction coefficient storage units  213 ,  223 , and  233  are performed by the learning process performed as pre-processing of the actual image correction process. This process will be described with reference to  FIG. 7  and the subsequent figures. 
       FIG. 7  is a diagram illustrating an example of the process of calculating the class correspondence correction coefficients (parameters) to be stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233 . 
     A sample high-quality image  301  is input. 
     It should be noted that, desirably, the number of sample high-quality images  301  to be input is not one, but two or more. 
     A large number of image data items having various characteristics are input as sample images and the learning process is performed. 
     That is, a large number of image data items having various characteristics are input, the learning process is performed, and correction coefficients (parameters) according to classes corresponding to different feature amounts generated as the result of learning are stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233 . 
     The first-stage to third-stage degradation-simulated image generation units  211  to  231  illustrated in  FIG. 7  generate images with the same degradation levels as the first-stage to third-stage degradation-simulated image generation units  211  to  231  of the image correction unit  127  illustrated in  FIG. 6 . 
     For example, the first-stage to third-stage degradation-simulated image generation units  211  to  231  apply an LPF to generate degraded images. 
     Here, an example of a process in a case in which a low-quality image which is the image to be corrected and is input to the image correction unit  127  in the actual image correction process is an image having a resolution that is one eighth of the resolution of a high-quality image input to the image correction unit  127  will be described. 
     In this case, the first-stage degradation-simulated image generation unit  211  generates a ⅛-resolution degradation-simulated image  302  having a resolution that is one eighth of the resolution of the sample high-quality image  301 . 
     That is, the first-stage degradation-simulated image generation unit  211  generates a degraded image with a resolution level that is substantially equal to the resolution level of the low-quality image which is the image to be corrected and is input to the image correction unit  127  in the actual image correction process. 
     The second-stage degradation-simulated image generation unit  221  generates a ¼-resolution degradation-simulated image  303  having a resolution that is a quarter of the resolution of the sample high-quality image  301 . 
     In addition, the third-stage degradation-simulated image generation unit  231  generates a ½-resolution degradation-simulated image  304  having a resolution that is half the resolution of the sample high-quality image  301 . 
     As such, in the first to third stages, the images whose degradation levels are sequentially reduced are generated. 
     The learning process execution unit  320  performs the learning process of calculating the class correspondence correction coefficient (parameter) using these images. 
     The learning process execution unit  320  performs a process in Steps S 11  to S 13  illustrated in  FIG. 7 . 
     It should be noted that the process in Steps S 11  to S 13  may be performed sequentially or in parallel. 
     In Step S 11 , the ⅛-resolution degradation-simulated image  302  and the ¼-resolution degradation-simulated image  303  are input, the image feature amount of each predetermined pixel region (local region) of each image is calculated, and a correction coefficient (correction parameter) corresponding to the image feature amount is calculated by the learning process. 
     That is, a supervised learning process using the ¼-resolution degradation-simulated image  303  as a teacher image (restored image) and the ⅛-resolution degradation-simulated image  302  as a student image is performed to acquire the optimum correction coefficients (correction parameters) corresponding to various feature amounts. 
     A specific example of the process will be described with reference to  FIG. 8 . 
     As illustrated in  FIG. 8 , rectangular pixel regions (local regions A and B) that have, for example, several pixels to several tens of pixels and are at a corresponding pixel position which is the same coordinate position are extracted from the ⅛-resolution degradation-simulated image  302  which is a student image and the ¼-resolution degradation-simulated image  303  which is a teacher image (restored image) and the feature amount of the pixel region, for example, brightness distribution information is acquired. 
     In addition, a correction coefficient (correction parameter) for converting the value of a central pixel of the local region A of the ⅛-resolution degradation-simulated image  302  which is a student image into the value of a central pixel of the local region B of the ¼-resolution degradation-simulated image  303  which is a teacher image (restored image) is calculated. 
     The correction coefficient calculation process is performed for all of various sample images images. 
     The learning process using many sample images is performed to calculate the optimum correction coefficients corresponding to a large number of different feature amounts. 
     It should be noted that the correction coefficient (correction parameter) is calculated for each class corresponding to the classification information of the feature amount. 
     The correction coefficients corresponding to each class are stored in the first-stage class correspondence correction coefficient storage unit  213 . 
     It should be noted that various feature amounts can be applied as the feature amounts of each region of the image. 
     For example, this will be described with reference to  FIG. 9 . In the example illustrated in  FIG. 9 , a pixel value (brightness) distribution of each region (each local region) of the image is used as the feature amount. 
       FIG. 9 ( 1 ) illustrates an example of the pixel value distribution of the image before and after restoration. 
     In a graph illustrated in  FIG. 9 ( 1 ), a solid line indicates the pixel value distribution of the local region of the image after restoration, that is, the ¼-resolution degradation-simulated image  303  which is a teacher image. 
     In contrast, in the graph illustrated in  FIG. 9 ( 1 ), a dotted line indicates the pixel value distribution of the local region of the image before restoration, that is, the ⅛-resolution degradation-simulated image  302  which is a student image. 
     The pixel value distribution of the local region of the ⅛-resolution degradation-simulated image  302  which is a student image is gentler than the pixel value distribution of the local region of the ¼-resolution degradation-simulated image  303  which is a teacher image. That is, pixel value distribution of the local region of the 1 the ⅛-resolution degradation-simulated image  302  is unclear. 
     As illustrated in  FIG. 9 ( 1 ), the amplitudes of the two images are clearly different from each other. 
       FIG. 9 ( 1 ) illustrates the following two amplitudes: 
     (a) A pixel value amplitude of the image before restoration (=the amplitude of the student image); and 
     (b) A pixel value amplitude of the image after restoration (=the amplitude of the teacher image). 
     For example, class classification is performed on the basis of a combination of the data items in (a) and (b). 
       FIG. 9 ( 2 ) is a diagram illustrating an example of the class classification. 
     The diagram illustrated in  FIG. 9 ( 2 ) shows an example in which (b) the pixel value amplitude of the image after restoration (=the amplitude of the teacher image) is set to the horizontal axis and (a) the pixel value amplitude of the image before restoration (=the amplitude of the student image) is set to the vertical axis and a class ID (identifier) is set to each predetermined classified region. 
     For example, as such, class classification is performed on the basis of the feature amount (the brightness distribution of the local region). 
     The learning processing unit  320  illustrated in  FIG. 7  calculates a correction coefficient (correction parameter) for each class, that is, a correction coefficient (correction parameter) for converting the pixel value of each region (local region) of the ⅛-resolution degradation-simulated image  302  which is a student image into the pixel value of a corresponding position of the ¼-resolution degradation-simulated image  303  which is a teacher image (restored image) and stores the calculated correction coefficient as the correction coefficient (correction parameter) corresponding to each class (class ID) in the first-stage class correspondence correction coefficient storage unit  213 . 
       FIG. 10  is a diagram illustrating an example of the correction coefficients stored in the first-stage class correspondence correction coefficient storage unit  213 . 
     The example of the correction coefficients stored in the first-stage class correspondence correction coefficient storage unit  213  illustrated in  FIG. 10  is an example in a case in which the correction performed by the first-stage correction unit  215  of the image correction unit  127  illustrated in  FIG. 6  is correction for calculating a corrected pixel value y in accordance with the following (Expression 1) as illustrated in  FIG. 10 . 
     
       
         
           
             
               
                 
                   
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     It should be noted that each symbol in the above-mentioned (Expression 1) has the following meaning: 
     y: a correction value of the pixel to be corrected; 
     x i : a value of the reference pixel; 
     i: an identifier of the reference pixel; and 
     k i : a multiplication coefficient corresponding to the reference pixel i. 
     The correction coefficients stored in the first-stage class correspondence correction coefficient storage unit  213  illustrated in  FIG. 10  are a set of the multiplication coefficients k i  corresponding to the reference pixel i applied to the above-mentioned (Expression 1). 
     The set of the multiplication coefficients k i  corresponding to the reference pixel i in (Expression 1) corresponds to, for example, the multiplication coefficients of the reference pixel set to the LPF. 
     That is, for example, the image correction unit in each processing stage performs the correction process using the LPF which multiplies the value of the reference pixel by the multiplication coefficient k i  to calculate the correction value of the central pixel value to be corrected. 
     The multiplication coefficient k i  is data that varies in accordance with the class corresponding to the feature amount. 
     The correction coefficient (ki) corresponding to the class is calculated by the learning process described with reference to  FIGS. 7 to 9 . 
     It should be noted that the position of the reference pixel x i  in the above-mentioned (Expression 1) is decided in the tap selection units  214 ,  224 , and  234  of the image correction unit  127  illustrated in  FIG. 6 . 
     As such, in Step S 11 , the learning processing unit  320  illustrated in  FIG. 7  receives the ⅛-resolution degradation-simulated image  302  and the ¼-resolution degradation-simulated image  303  and detects the feature amount of each predetermined pixel region (local region) of each image. In addition, the learning process execution unit  320  calculates the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the ¼-resolution image from the ⅛-resolution image, using the learning process, and stores the correction coefficient in the first-stage class correspondence correction coefficient storage unit  213 . 
     Similarly, in Step S 12 , the learning processing unit  320  illustrated in  FIG. 7  receives the ¼-resolution degradation-simulated image  303  and the ½-resolution degradation-simulated image  304 , calculates the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the ½-resolution image from the ¼-resolution image, using the learning process, and stores the correction coefficient in the second-stage class correspondence correction coefficient storage unit  223 . 
     That is, in Step S 12 , the learning process execution unit  320  performs the learning process in which the ¼-resolution degradation-simulated image  303  is set as a student image and the ½-resolution degradation-simulated image  304  as a teacher image. 
     In the learning process, the feature amount of each predetermined pixel region (local region) of each image is detected. The optimum correction coefficient corresponding to the feature amount (=corresponding to the class) for restoring the ½-resolution image from the ¼-resolution image is calculated by the learning process and is stored in the second-stage class correspondence correction coefficient storage unit  223 . 
     Similarly, in Step S 13 , the learning processing unit  320  illustrated in  FIG. 7  receives the ½-resolution degradation-simulated image  304  and the original sample high-quality image  301 , calculates the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the original sample high-quality image from the ½-resolution image, using the learning process, and stores the correction coefficient in the third-stage class correspondence correction coefficient storage unit  233 . 
     That is, in Step S 13 , the learning process execution unit  320  performs the learning process in which the ½-resolution degradation-simulated image  304  is set as a student image and the original sample high-quality image  301  (=1/1 resolution) as a teacher image. 
     In the learning process, the feature amount of each predetermined pixel region (local region) of each image is detected. The optimum correction coefficient corresponding to the feature amount (corresponding to the class) for restoring an image having the resolution (1/1 resolution) of the original sample image  301  from the ½-resolution image is calculated by the learning process and is stored in the third-stage class correspondence correction coefficient storage unit  233 . 
     With this configuration, the following correction coefficients are stored in the correction coefficient storage units  213 ,  223 , and  233  of the image correction unit  127  illustrated in  FIG. 6  by the learning process which is performed in advance using the input sample image: 
     (1) The first-stage class correspondence correction coefficient storage unit  213 =the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the ¼-resolution image from the ⅛-resolution image; 
     (2) The second-stage class correspondence correction coefficient storage unit  223 =the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the ½-resolution image from the ½-resolution image; and 
     (3) The third-stage class correspondence correction coefficient storage unit  233 =the correction coefficient corresponding to the class which corresponds to the optimum feature amount for restoring the 1/1-resolution image from the ½-resolution image. 
     After the correction coefficients corresponding to the classes are stored by the learning process in advance, the actual image to be corrected, that is, the positioned low-quality image  162  illustrated in  FIG. 6  and the positioning high-quality image  161  which is a high-quality image obtained by capturing the same image are input to the image correction unit  127  illustrated in  FIG. 6  and the image correction unit  127  performs a correction process, that is, a quality improvement process for the positioned low-quality image  162 . 
     [5. For Image Correction Process Using Class Correspondence Correction Coefficient Calculated by Learning Process] 
     Next, the image correction process using the class correspondence correction coefficients calculated by the learning process will be described. 
     In the image correction unit  127  illustrated in  FIG. 6 , the class correspondence correction coefficients calculated by the learning process are stored in the storage units, that is, the first-stage to third-stage class correspondence correction coefficient storage units  213  to  233 . 
     As such, after the correction coefficients are stored in each storage unit, the positioned low-quality image  162  which is the actual image to be corrected and the positioned high-quality image  161  having the same object as the positioned low-quality image  162  are input. 
     It should be noted that, as described above, the positioned low-quality image  162  is a low-resolution image such as a fluorescent image or a far-infrared image. 
     In contrast, the positioned high-quality image  161  is a high-resolution image such as a visible image. 
     As described above, the image correction unit  127  illustrated in  FIG. 6  has a multi-stage configuration of the first-stage processing unit  210 , the second-stage processing unit  220 , and the third-stage processing unit  230 . The processing result of the first-stage processing unit  210  is input to the second-stage processing unit  220 , the processing result of the second-stage processing unit  220  is input to the third-stage processing unit  230 , and the processing result of the third-stage processing unit  230  is output as the processing result of the image correction unit  127 . 
     For example, in a case in which the positioned low-quality image  162  which is the image to be corrected has a resolution that is one eighth of the resolution of the positioned high-quality image  161  which is the reference image, the processes performed in each processing stage are set as follows. 
     The first-stage processing unit  210  performs a quality improvement process of converting the ⅛-resolution positioned low-quality image  162  into a ¼-resolution image. 
     The second-stage processing unit  220  performs a quality improvement process of converting the ¼-resolution image input from the first-stage processing unit  210  into a ½-resolution image. 
     The third-stage processing unit  230  performs a quality improvement process of converting the ½-resolution image input from the second-stage processing unit  220  into a 1/1-resolution image. 
     The ⅛-resolution positioned low-quality image  162  which is the image to be corrected is output as a high-quality image having the same resolution as the positioned high-quality image  161  which is the reference image by the three-stage process. 
     A specific example of the process performed in the first-stage processing unit  210  of the image correction unit  127  illustrated in  FIG. 6  will be described with reference to  FIGS. 11 and 12 . 
       FIG. 11  illustrates the first-stage degradation-simulated image generation unit  211 , the first-stage class classification processing unit  212 , the first-stage class correspondence correction coefficient storage unit  213 , the first-stage tap selection unit  214 , and the first-stage image correction unit  215  which are components of the first-stage processing unit  210  and the second-stage degradation-simulated image generation unit  221  which is a component of the second-stage processing unit  210  in the image correction unit  127  illustrated in  FIG. 6 . 
     The positioned high-quality image  161  which is the reference image is input to the first-stage degradation-simulated image generation unit  211 , the second-stage degradation-simulated image generation unit  221 , and the third-stage degradation-simulated image generation unit  231  of the image correction unit  127  illustrated in  FIG. 6  and is converted into low-resolution images with different levels by processes using different low-pass filters (LPFs). 
     The first-stage degradation-simulated image generation unit  211  generates a degradation-simulated image having a resolution that is one eighth of the resolution of the positioned high-quality image  161 , that is, the same resolution as the positioned low-quality image  162  which is the image to be corrected. 
     The second-stage degradation-simulated image generation unit  221  generates a degradation-simulated image having a resolution that is a quarter of the resolution of the positioned high-quality image  161 . 
     The third-stage degradation-simulated image generation unit  231  generates a degradation-simulated image having a resolution that is half the resolution of the positioned high-quality image  161 . 
     The first-stage degradation-simulated image generation unit  211  illustrated in  FIG. 11  inputs the degradation-simulated image having a resolution that is one eighth of the resolution of the positioned high-quality image  161  to the first-stage class classification processing unit  212 . 
     In addition, the second-stage degradation-simulated image generation unit  221  illustrated in  FIG. 11  inputs the degradation-simulated image having a resolution that is a quarter of the resolution of the positioned high-quality image  161  to the first-stage class classification processing unit  212 . 
     The first-stage class classification processing unit  212  receives the two degradation-simulated images, detects the feature amount of each local region, and specifies a class corresponding to the detected feature amount. 
     The first-stage image correction unit  215  acquires a correction coefficient corresponding to the class specified by the first-stage class classification processing unit  212  from the first-stage class correspondence correction coefficient storage unit  213  and performs a process of correcting the pixel value of the positioned low-quality image  162 . 
     It should be noted that the first-stage tap selection unit  214  performs a tap selection process as a process in a stage before the pixel value correction process of the first-stage image correction unit  215 . The tap selection process is a process of selecting the reference pixel used to calculate the value of the pixel to be corrected. 
     For example, as illustrated in (Example 1) and (Example 2) in a lower part of  FIG. 11 , the reference pixel is selected from the pixels around one pixel to be corrected. 
     It should be noted that the setting that has been united in advance may be applied as the tap setting to all corrections or the tap setting may be changed in accordance with the class corresponding to the feature amount detected by the first-stage class classification processing unit  212 . 
     In the example illustrated in  FIG. 11 , the correction performed by the first-stage image correction unit  215  is pixel value correction according to the above-mentioned (Expression 1). That is, the corrected pixel value y is calculated in accordance with the following (Expression 1). 
     
       
         
           
             
               
                 
                   
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     It should be noted that each symbol in the above-mentioned (Expression 1) has the following meaning: 
     y: a correction value of the pixel to be corrected; 
     x i : a value of the reference pixel; 
     i: an identifier of the reference pixel; and 
     k i : a multiplication coefficient corresponding to the reference pixel i. 
     Here, k i  (the multiplication coefficient corresponding to the reference pixel i) that is used at this time is the correction coefficient corresponding to a specific class acquired from the first-stage class correspondence correction coefficient storage unit  213 . 
     That is, the correction coefficient corresponding to the class which has been specified by the first-stage class classification processing unit  212  on the basis of the feature amounts of the local regions of the ⅛-resolution degradation-simulated image generated by the first-stage degradation-simulated image generation unit  211  and the ¼-resolution degradation-simulated image is used. 
     It should be noted that, for example, as described with reference to  FIG. 9 , a class number is set in advance by a combination of the feature amounts of the two degradation-simulated images and the correction coefficients associated with the class number are acquired from the first-stage class correspondence correction coefficient storage unit  213 . 
     In addition, a specific example of the pixel value correction process performed by the first-stage processing unit  210  will be described with reference to  FIG. 12 . 
     Similarly to  FIG. 11  that has been described above,  FIG. 12  illustrates the first-stage degradation-simulated image generation unit  211 , the first-stage class classification processing unit  212 , the first-stage class correspondence correction coefficient storage unit  213 , the first-stage tap selection unit  214 , and the first-stage image correction unit  215  which are components of the first-stage processing unit  210  and the second-stage degradation-simulated image generation unit  221  which is a component of the second-stage processing unit  210  in the image correction unit  127  illustrated in  FIG. 6 . 
     The first-stage degradation-simulated image generation unit  211  generates a ⅛-resolution degradation-simulated image  331  having the same resolution as the positioned low-quality image  162  which is the image to be corrected as a degradation-simulated image of the positioned high-quality image  161 . 
     The second-stage degradation-simulated image generation unit  221  generates a ¼ degradation-simulated image  332  having a resolution that is a quarter of the resolution of the positioned high-quality image  161 . 
     The first-stage class classification processing unit  212  receives the two degradation-simulated images, detects the feature amount of each local region, and specifies a class corresponding to the detected feature amount. 
     In the example illustrated in  FIG. 12 , the feature amounts of a local region A of the ⅛-resolution degradation-simulated image  331  and a local region B of the ¼ degradation-simulated image  332  are extracted. 
     For example, as described with reference to  FIG. 9 , the pixel value (brightness) distribution of the local region is extracted from the local regions A and B. 
     In addition, a class corresponding to the extracted feature amount is identified in accordance with the class classification information described with reference to  FIG. 9 ( 2 ). 
     The first-stage image correction unit  215  acquires the correction coefficient corresponding to the class specified by the first-stage class classification processing unit  212  from the first-stage class correspondence correction coefficient storage unit  213 . 
     The acquired correction coefficient is a class correspondence correction coefficient  340  illustrated in  FIG. 12 . 
     The first-stage image correction unit  215  performs a process of correcting the pixel value of the positioned low-quality image  162 , using the class correspondence correction coefficient  340 . 
     It should be noted that a tap selection process of the first-stage tap selection unit  214  is performed as a process in a stage before the pixel value correction process of the first-stage image correction unit  215 . As described above, the tap selection process is a process of selecting the reference pixel used to calculate the value of the pixel to be corrected. 
     For example, the setting of selecting an optimum tap (reference pixel range) in accordance with the feature amounts in the vicinity of the correction pixel can be performed. 
     It should be noted that, for example, the feature amount acquired from the degraded image of the positioned high-quality image  161  in the first-stage class classification processing unit  212  can be used as the feature amount. 
     The reference pixel range is specified by the selection of the tap by the first-stage tap selection unit  214  and the value of the pixel to be corrected is calculated using the value of the reference pixel in the reference pixel range and the correction coefficient acquired from the first-stage class correspondence correction coefficient storage unit  213 . 
     For example, the correction value of the pixel to be corrected in a positioned low-quality image (before first-stage correction)  162   a  illustrated in  FIG. 12  is calculated in accordance with the above-mentioned (Expression 1). 
     The first-stage image correction unit  215  generates a positioned low-quality image (after first-stage correction)  162   b  having the corrected pixel. 
     The first-stage processing unit  210  performs the pixel value correction for all of the pixels forming the positioned low-quality image  162 . 
     It should be noted that, in this process, the local regions A and B of the degradation-simulated images  331  and  332  generated from the positioned high-quality image  161  are the same coordinate regions as the pixel regions having, as the center, the pixel to be corrected in the positioned low-quality image  162  which is the image to be corrected. 
     That is, the process is performed using the captured regions of the same object. 
     The first-stage processing unit  210  performs a quality improvement process for all of the pixels forming the positioned low-quality image  162  to convert the ⅛-resolution positioned low-quality image  162  into a ¼-resolution image. 
     The processed image is input to the second-stage processing unit  220 . 
     The second-stage processing unit  220  performs a quality improvement process of converting the ¼-resolution image input from the first-stage processing unit  210  into a ½-resolution image. 
     In the second-stage processing unit  220 , in this process, the second-stage class classification processing unit  222  of the second-stage processing unit  220  receives the ¼ degradation-simulated image which has a resolution that is a quarter of the resolution of the positioned high-quality image  161  and has been generated by the second-stage degradation-simulated image generation unit  221  and the ½ degradation-simulated image which has a resolution that is half the resolution of the positioned high-quality image  161  and has been generated by the third-stage degradation-simulated image generation unit  231 , detects the feature amount of each local region, and specifies a class corresponding to the detected feature amount. 
     The second-stage image correction unit  225  acquires a correction coefficient corresponding to the class specified by the second-stage class classification processing unit  222  from the second-stage class correspondence correction coefficient storage unit  223 . 
     The second-stage image correction unit  225  performs pixel value correction for the low-quality image with a ¼-resolution level input from the first-stage processing unit  210 , using the class correspondence correction coefficient. 
     A quality improvement process of converting the positioned low-quality image  162  into a ½-resolution image is performed by this correction process. 
     The processed image is input to the third-stage processing unit  230 . 
     The third-stage processing unit  230  performs a quality improvement process of converting the ½-resolution image input from the second-stage processing unit  220  into a 1/1-resolution image. That is, the third-stage processing unit  230  performs a process of improving the resolution of the input image to the same resolution level as that of the positioned high-quality image  161  used as the reference image. 
     In the third-stage processing unit  230 , in this process, the third-stage class classification processing unit  232  of the third-stage processing unit  230  receives the ½ degradation-simulated image which has a resolution that is half the resolution of the positioned high-quality image  161  and has been generated by the third-stage degradation-simulated image generation unit  231  and the positioned high-quality image  161  which is an input image, detects the feature amount of each local region, and specifies a class corresponding to the detected feature amount. 
     The third-stage image correction unit  235  acquires a correction coefficient corresponding to the class specified by the third-stage class classification processing unit  232  from the third-stage class correspondence correction coefficient storage unit  233 . 
     The third-stage image correction unit  235  performs pixel value correction for the low-quality image with a ½-resolution level input from the second-stage processing unit  220 , using the class correspondence correction coefficient. 
     A quality improvement process of converting the positioned low-quality image  162  into a 1/1-resolution image is performed by this correction process. 
     That is, the third-stage processing unit  230  performs a process of improving the resolution of the input image to the same resolution level as that of the positioned high-quality image  161  used as the reference image. 
     As such, the image correction unit of the image processing apparatus according to the present disclosure performs the pixel value correction as a multi-stage process (cascade process) as described with reference to the drawings including  FIG. 6 . 
     This is to apply accurate correction coefficients corresponding to a larger number of classes to the processing units in each stage. 
     As described above, the correction coefficients are classified in accordance with the classes by the learning process which has been performed in advance. The above-mentioned process is performed in order to obtain accurate correction coefficients corresponding to a larger number of classes, that is, the optimum correction coefficients corresponding to the image feature amounts. 
     The number of classes that can be used by the image correction unit  127  according to this configuration will be described with reference to FIG.  13 . 
       FIG. 13  illustrates the same configuration as that of the image correction unit  127  described with reference to  FIG. 6 . 
     The image correction unit  127  illustrated in  FIG. 13  includes three-stage correction processing units in the first to third stages. In a case in which the number of classes associated with different correction coefficients in each stage is 1000, in three-stage correction processes in the first to third stages, correction can be performed using different correction coefficients corresponding to 1000×1000×1000=1000000000, that is, 1K×1K×1K=1G classes. 
     That is, correction using accurate correction coefficients corresponding to finely classified image characteristics is achieved. 
     In addition, in the tap selection process, tap selection can be performed by the first-stage to third-stage tap selection units  214  in three stages. 
     The tap is a reference pixel range that is applied to calculate the corrected pixel value. As described above, for example, the setting of selecting the optimum tap (reference pixel range) in accordance with the feature amounts in the vicinity of the correction pixel can be performed. 
     The tap selection is performed in three times to select the optimum tap corresponding to the feature amount detected in each stage. 
     The image processing apparatus according to the present disclosure performs the optimum correction corresponding to the feature amount of each local region of the image, using the above-mentioned process. 
     6. FOR OTHER EMBODIMENTS 
     Next, embodiments different from the above-described embodiment will be described. 
     The following two items will be sequentially described: 
     (1) For Variation in Feature Amount Used for Image Correction Process; and 
     (2) For Modification Examples of Multi-stage Configuration. 
     [6-1. For Example of Feature Amount Usable for Image Correction Process] 
     First, an example of the feature amount usable for the image correction process will be described. 
     In the above-described embodiment, in the first-stage to third-stage class classification processing units  212 ,  222 , and  232  of the image correction unit  127  illustrated in  FIG. 6 , the feature amounts acquired from each degradation-simulated image are used as the pixel value (brightness) distribution information of the local regions as described with reference to  FIG. 9 . 
     In the learning process, the pixel value (brightness) distribution information of the local region is acquired from the sample image and the correction coefficient based on the feature amount is calculated. 
     In addition, in the low-quality image correction process, the pixel value (brightness) distribution information of the local region is acquired from the high-quality image which has been input as the reference image in parallel and correction is performed using the correction coefficient corresponding to the class which corresponds to the feature amount subjected to class classification in the learning process before the correction process. 
     The feature amount that can be used for the image correction process performed as the quality improvement process in the image processing apparatus according to the present disclosure is not limited to, for example, the pixel value distribution described with reference to  FIG. 9  and the process may be performed using various feature amounts. 
     A plurality of examples of the feature amount applied to the quality improvement process in the image processing apparatus according to the present disclosure will be described with reference to  FIG. 14  and the subsequent figures. 
       FIG. 14  illustrates an example of the image feature amounts which are acquired from each degradation-simulated image and are used to set classes associated with correction coefficients in the first-stage to third-stage class classification processing units  212 ,  222 , and  232  of the image correction unit  127  illustrated in  FIG. 6 . 
       FIG. 14  illustrates the following three types of image feature amounts: 
     (1) Brightness distribution information; 
     (2) A point spread function (PSF) (=a function indicating a blurred state); and 
     (3) Noise information. 
     “(1) The brightness distribution information” is distribution information of the brightness value of each pixel in an image. A specific example illustrated in  FIG. 14 ( 1 )( b ) shows a graph (brightness distribution graph) in which a pixel position is set to the horizontal axis and a brightness value is set to the vertical axis. 
     In the example illustrated in  FIG. 14 ( 1 )( b ), the left side of the graph is a low brightness value and the right side is a high brightness value. This brightness distribution is, for example, a brightness distribution corresponding to an edge region such as the boundary of an object. 
     It should be noted that this feature amount corresponds to the feature amount described with reference to  FIG. 9  and is an image feature amount which can be acquired from the positioned high-quality image  161  or the degradation-simulated image thereof. 
     “(2) The point spread function (PSF) (=the function indicating a blurred state)” is a point spread function (PSF) which is a function indicating the amount of blurring of an image. 
     As illustrated in a specific example of  FIG. 14 ( 2 )( b ), the point spread function is a function indicating the degree of spread of a pixel value at a certain pixel position to the surrounding pixel values, that is, the amount of blurring. 
     It should be noted that the point spread function is also an image feature amount which can be acquired from the positioned high-quality image  161  or the degradation-simulated image thereof. 
     “(3) The noise information” is information indicating noise included in an image. An image captured by the camera has a certain amount of noise. 
     A specific example illustrated in  FIG. 14 ( 3 )( b ) shows a graph (noise distribution graph) in which a pixel position is set to the horizontal axis and a pixel value is set to the vertical axis. 
     As illustrated in the graph, the pixel value is a value obtained by adding a predetermined amount of noise to the original color or brightness of an object. It should be noted that there are various types of noise such as high-frequency noise and low-frequency noise. 
     It should be noted that the noise information is also an image feature amount which can be acquired from the positioned high-quality image  161  or the degradation-simulated image thereof. 
     The three image feature amounts illustrated in  FIG. 14  are feature amounts that can be acquired from the sample image in the learning process described with reference to  FIG. 7  and are feature amounts that can be acquired from the positioned high-quality image  161  or the degradation-simulated image thereof in the first-stage to third-stage class classification processing units  212 ,  222 , and  232  of the image correction unit  127  illustrated in  FIG. 6 . 
     The processes using the feature amounts illustrated in  FIGS. 14 ( 1 ) to  14 ( 3 ) include a learning process based on the sample image and a process using the same feature amounts as the actual quality improvement process for the low-quality image. 
     For example, in a case in which the point spread function (PSF) which is a function indicating the amount of blurring of an image illustrated in FIG.  14 ( 2 ) is used as the feature amount, in the learning process that is performed in advance, a point spread function (PSF) which is a function indicating the amount of blurring of a local region is acquired as the feature amount from the sample image and a correction coefficient subjected to class classification in accordance with the amount of blurring is calculated as a correction coefficient for reducing the amount of blurring on the basis of the feature amount and is then stored in the storage unit (class correspondence correction coefficient storage unit). 
     In addition, in the low-quality image correction process, a point spread function (PSF) which is a function indicating the amount of blurring is acquired as the feature amount of a local region from the high-quality image which has been input in parallel as the reference image and correction is performed using the correction coefficient corresponding to the feature amount correspondence class subjected to the class classification in the learning process performed in advance. 
     The image correction units in each processing stage perform, for example, a correction process using an LPF. 
     The correction coefficient corresponding to the feature amount correspondence class subjected to the class classification is, for example, a multiplication coefficient of the reference pixel set to the LPF. 
     In addition, for example, in a case in which the amount of noise of an image illustrated in  FIG. 14 ( 3 ) is used as the feature amount, in the learning process that is performed in advance, the amount of noise of a local region is acquired as the feature amount from the sample image and a correction coefficient subjected to class classification in accordance with the amount or type (low frequency/high frequency) of noise is calculated as a correction coefficient for reducing the amount of noise on the basis of the feature amount and is then stored in the storage unit (class correspondence correction coefficient storage unit). 
     Further, in the low-quality image correction process, the amount or type of noise is acquired as the feature amount of a local region from the high-quality image which has been input in parallel as the reference image and correction is performed using the correction coefficient corresponding to the feature amount correspondence class subjected to the class classification in the learning process performed in advance. 
     The image correction units in each processing stage perform, for example, a correction process decreasing the noise. 
     Furthermore, a plurality of different feature amounts may be acquired, a correction coefficient for improving image quality may be calculated in accordance with the acquired plurality of feature amounts, and the correction process may be performed using the correction coefficient. 
     As such, in the image processing apparatus according to the present disclosure, it is possible to apply various feature amounts. 
     [6-2. For Other Examples of Multi-Stage Configuration of Image Correction Unit] 
     In the above-described embodiment, as described with reference to  FIG. 6 , the example in which the image correction unit  127  has a three-stage configuration of the first-stage to third-stage processing units  210  to  230  and repeats the process of performing image processing in three times. 
     The number of stages can be set in the image correction unit  127  in various manners. 
     Various configurations, such as a two-stage configuration and a configuration with four or more stages, are possible. 
       FIG. 15  illustrates an example of the configuration of the image correction unit  127 . 
     The image correction unit  127  illustrated in FIG.  15  has an n-stage configuration of first-stage to n-th-stage processing units  410 - 1  to  410 - n  and repeats a process of performing image processing n times. 
     The processing units in each stage have the same components and include the following elements: 
     Degradation-simulated image generation units  411 - 1  to  411 - n;    
     Class classification processing units  412 - 1  to  412 - n;    
     Class correspondence correction coefficient storage units  413 - 1  to  413 - n;    
     Tap selection units  414 - 1  to  414 - n ; and 
     Image correction units  415 - 1  to  415 - n.    
     Correction using accurate correction coefficients corresponding to finely classified image characteristics is achieved by the increase in the number of stages. 
     In addition, in the tap selection process, in each stage, tap selection is possible in various settings and tap selection most suitable for characteristics is possible. 
     7. FOR SEQUENCE OF PROCESS PERFORMED BY IMAGE PROCESSING APPARATUS 
     Next, the sequence of the process performed by the image processing apparatus according to the present disclosure will be described with reference to flowcharts illustrated in  FIG. 16  and the subsequent figures. 
     As described in the embodiment, the image processing apparatus according to the present disclosure performs a process which performs the learning process with the sample image to calculate the correction coefficient corresponding to the feature amount based on the learning process, that is, the class correspondence correction coefficient and stores the class correspondence correction coefficient in the class correspondence correction coefficient storage unit which is a storage unit before the correction process for the actual image to be corrected. 
     The flowchart illustrated in  FIG. 16  is a flowchart illustrating the sequence of the learning process. 
     The flowchart illustrated in  FIG. 17  is a flowchart illustrating the sequence of the image correction process performed after the learning process ends. 
     First, the sequence of the learning process, that is, the sequence of the process which performs the learning process with the sample image to calculate the correction coefficient corresponding to the feature amount based on the learning process, that is, the class correspondence correction coefficient and stores the class correspondence correction coefficient in the class correspondence correction coefficient storage unit which is a storage unit will be described with reference to the flowchart illustrated in  FIG. 16 . 
     For example, the process according to the flow illustrated in  FIG. 16  is performed under the control of a control unit having a program execution function according to the program stored in the storage unit of the image processing apparatus. 
     Hereinafter, processes in each step of the flow illustrated in  FIG. 16  will be sequentially described. 
     (Step S 101 ) 
     First, in Step S 101 , the image processing apparatus inputs a sample image. 
     That is, the image processing apparatus inputs a sample image for performing a feature amount extraction process. The sample image is, for example, a high-quality image with high resolution such as a visible image. 
     It should be noted that, as described with reference to  FIG. 7 , one high-quality sample image is not input, but a plurality of image data items having various characteristics are input. That is, a plurality of image data items having various characteristics are input, the learning process is performed, and correction coefficients (parameters) according to classes corresponding to different feature amounts generated as the results of learning are stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233  as illustrated in  FIG. 7 . 
     (Step S 102 ) 
     Then, in Step S 102 , the image processing apparatus performs a degraded image generation process. 
     This process is the process performed by the degradation-simulated image generation units  211 ,  221 , and  231  described with reference to  FIG. 7 . 
     For example, the image processing apparatus generates degraded images with different degradation levels using different low-pass filters (LPFs). 
     It should be noted that the highest degradation level is desirably exactly equal to the degradation level of the image to be corrected. 
     (Step S 103 ) 
     Then, in Step S 103 , the image processing apparatus performs a process of extracting a feature amount from the input sample image or the degraded image thereof. 
     This process is the process performed by the learning process execution unit  320  described with reference to  FIG. 7 . 
     For example, this process corresponds to a portion of the process in Steps S 11  to S 13  described with reference to  FIG. 7 . 
     In Step S 11  described with reference to  FIG. 7 , the ⅛-resolution degradation-simulated image  302  and the ¼-resolution degradation-simulated image  303  are input and the image feature amount of each predetermined pixel region (local region) of each image is calculated. 
     (Step S 104 ) 
     Then, in Step S 104 , the image processing apparatus calculates a correction coefficient (correction parameter) corresponding to the image feature amount extracted in Step S 103  using the learning process. 
     This process also corresponds to a portion of the process in Steps S 11  to S 13  described with reference to  FIG. 7 . 
     In Step S 11  described with reference to  FIG. 7 , the supervised learning process in which the ¼-resolution degradation-simulated image  303  is a teacher image (restored image) and the ⅛-resolution degradation-simulated image  302  is a student image is performed to acquire the optimum correction coefficients (correction parameters) corresponding to various feature amounts. 
     This correction coefficient calculation process is performed for all of various sample images. 
     It should be noted that the learning process using a larger number of sample images is performed to calculate the optimum correction coefficients corresponding to a large number of different feature amounts. 
     (Step S 105 ) 
     Then, in Step S 105 , the image processing apparatus stores the correction coefficients (correction parameters) calculated in Step S 104  as the correction coefficients which correspond to each class corresponding to the classification information of the feature amounts in the storage unit, that is, the first-stage class correspondence correction coefficient storage unit  213  illustrated in  FIG. 7 . 
     (Step S 106 ) 
     Then, in Step S 106 , the image processing apparatus determines whether feature amount extraction for the scheduled degraded images with all levels has been completed. 
     In a case in which there is an unprocessed degraded image, the process in Step S 102  and the subsequent steps is repeatedly performed for the unprocessed image. 
     This corresponds to the process of sequentially performing Steps S 11  to S 13  illustrated in  FIG. 7 . 
     In a case in which it is determined in Step S 106  that the feature amount extraction for the scheduled degraded images with all levels has been completed, the image processing apparatus proceeds to Step S 107 . 
     (Step S 107 ) 
     Then, in Step S 107 , the image processing apparatus determines whether the process for all of the scheduled sample images has ended. 
     In a case in which there is an unprocessed sample image, the image processing apparatus returns to Step S 101  and performs the process in Step S 101  and the subsequent steps for the unprocessed sample image. 
     As described above, it is desirable that the sample images from which feature amounts are extracted are a large number of image data items having various characteristics. That is, a large number of image data items having various characteristics are input, the learning process is performed, and correction coefficients (parameters) according to classes corresponding to different feature amounts generated as the results of learning are stored in the class correspondence correction coefficient storage units  213 ,  223 , and  233  as illustrated in  FIG. 7 . 
     In a case in which it is determined in Step S 107  that the process for all of the scheduled sample images has ended, the image processing apparatus ends the process. 
     It should be noted that, as described above, various feature amounts can be applied as the feature amounts acquired from the sample image in the learning process and various feature amounts described with reference to  FIG. 9  or  FIG. 14  can be extracted. 
     Next, the sequence of the image correction process performed after the learning process will be described with reference to the flowchart illustrated in  FIG. 17 . 
     The process according to the flow illustrated in  FIG. 17  is performed under the control of the control unit having the program execution function according to the program stored in the storage unit of the image processing apparatus. 
     Hereinafter, processes in each step of the flow illustrated in  FIG. 17  will be sequentially described. 
     (Step S 201 ) 
     First, in Step S 201 , the image processing apparatus inputs a low-quality image which is an image to be corrected and a high-quality image which is a reference image. 
     It should be noted that the images correspond to the positioned high-quality image  161  and the positioned low-quality image  162  illustrated in  FIG. 6  and are two images of the same positioned object. 
     (Step S 202 ) 
     Then, in Step S 202 , the image processing apparatus generates the degraded images of the high-quality image which is the reference image. 
     This process is the process performed by the first-stage degradation-simulated image generation unit  211 , the second-stage degradation-simulated image generation unit  221 , and the third-stage degradation-simulated image generation unit  231  of the image correction unit  127  illustrated in  FIG. 6 . 
     The high-quality image which is the reference image is input to the first-stage degradation-simulated image generation unit  211 , the second-stage degradation-simulated image generation unit  221 , and the third-stage degradation-simulated image generation unit  231  and is converted into low-resolution images with different levels by processes using different low-pass filters (LPFs). 
     Specifically, the first-stage degradation-simulated image generation unit  211  generates a degradation-simulated image (for example, a degradation-simulated image having a resolution that is one eighth of the resolution of the high-quality image) having the same resolution level as the low-quality image which is the image to be corrected. 
     The second-stage degradation-simulated image generation unit  221  generates a degradation-simulated image (for example, a degradation-simulated image having a resolution that is a quarter of the resolution of the high-quality image) having a lower degradation level than the degraded image generated by the first-stage degradation-simulated image generation unit  211 . 
     In addition, the third-stage degradation-simulated image generation unit  231  generates a degradation-simulated image (for example, a degradation-simulated image having a resolution that is half the resolution of the high-quality image) having a lower degradation level than the degraded image generated by the second-stage degradation-simulated image generation unit  221 . 
     (Step S 203 ) 
     Then, in Step S 203 , the image processing apparatus selects a correction pixel region of the low-quality image which is the image to be corrected. 
     In Step S 203 , the image processing apparatus sequentially selects the pixel to be corrected from the low-quality image which is the image to be corrected. 
     This corresponds to, for example, the process of selecting the pixel to be corrected in the positioned low-quality image (before first-stage correction)  162   a  illustrated in  FIG. 12 . 
     (Step S 204 ) 
     Then, in Step S 204 , the image processing apparatus selects a region corresponding to the correction pixel region from the high-quality image which is the reference image or the degraded image thereof, extracts the feature amount of the selected region, and performs a class classification process. 
     This process is, for example, the process performed by the first-stage class classification processing unit  212  illustrated in  FIGS. 6, 11, and 12 . 
     As illustrated in  FIG. 12 , the first-stage class classification processing unit  212  receives two degradation-simulated images, that is, the ⅛-resolution degradation-simulated image  331  and the ¼ degradation-simulated image  332 , detects the feature amount of each local region, and specifies a class corresponding to the detected feature amount. 
     In the example illustrated in  FIG. 12 , the feature amounts of the local region A of the ⅛-resolution degradation-simulated image  331  and the local region B of the ¼ degradation-simulated image  332  are extracted. 
     This process is, for example, the process of extracting the pixel value (brightness) distribution of the local region from the local regions A and B as described with reference to  FIG. 9 . 
     In addition, the classes corresponding to the extracted feature amounts are identified in accordance with the class classification information described with reference to  FIG. 9 ( 2 ). 
     (Step S 205 ) 
     Then, in Step S 205 , the image processing apparatus performs a process of selecting a tap (setting a reference pixel region) based on the feature amount. 
     This process is, for example, the process performed by the first-stage tap selection unit  214  illustrated in  FIGS. 6, 11, and 12 . 
     As described with reference to  FIGS. 11 and 12 , the tap selection process of the first-stage tap selection unit  214  is performed as a process in a stage before the pixel value correction process of the first-stage image correction unit  215 . The tap selection process is a process of selecting the reference pixel used to calculate the value of the pixel to be corrected. 
     This tap selection can be decided on the basis of the feature amount extracted from the high-quality image or the degraded image thereof in Step S 204 . 
     For example, the following process is performed: a wide reference region (tap range) is set in a case in which the pixel value amplitude of the local region acquired as the feature amount is small; and a narrow reference region (tap range) is set in a case in which the pixel value amplitude of the local region acquired as the feature amount is large. 
     (Step S 206 ) 
     Then, in Step S 206 , the image processing apparatus acquires a correction coefficient which corresponds to the class corresponding to the feature amount extracted from the high-quality image or the degraded image thereof in Step S 204  from the storage unit. 
     This process is, for example, the process performed by the first-stage image correction unit  215  described with reference to  FIGS. 6, 11, and 12 . 
     The first-stage image correction unit  215  acquires the correction coefficient corresponding to the class specified by the first-stage class classification processing unit  212  from the first-stage class correspondence correction coefficient storage unit  213 . 
     The class correspondence correction coefficient  340  illustrated in  FIG. 12  is acquired as the correction coefficient. 
     The first-stage image correction unit  215  performs the process of correcting the pixel value of the low-quality image using the class correspondence correction coefficient  340 . 
     (Step S 207 ) 
     Then, in Step S 207 , the image processing apparatus performs an image correction process for the low-quality image, using the tap selected in Step S 205  and the correction coefficient corresponding to the feature amount, that is, the class correspondence correction coefficient acquired from the storage unit in Step S 206 . 
     For example, the image processing apparatus performs a process of calculating the corrected pixel value y using the above-mentioned (Expression 1), that is, the expression illustrated in  FIG. 11 . 
     (Step S 208 ) 
     Then, in Step S 208 , the image processing apparatus determines whether the pixel value correction process for the entire region of the low-quality image which is the image to be corrected has been completed. 
     In a case in which there is an unprocessed pixel, the image processing apparatus performs the process in Step S 203  and the subsequent steps for the unprocessed pixel. 
     In a case in which it is determined in Step S 208  that the pixel value correction process for the entire region of the low-quality image which is the image to be corrected has been completed, the image processing apparatus proceeds to Step S 209 . 
     (Steps S 209  and S 210 ) 
     Then, in Step S 209 , the image processing apparatus determines whether there is a processing unit in the next stage. 
     As described with reference to  FIG. 6 , the image correction unit  1237  of the image processing apparatus according to the present disclosure has a multi-stage configuration (cascade configuration) of a plurality of stages. 
     That is, the result of the correction process which is the result of the quality improvement process of the first-stage processing unit is input to the second-stage processing unit and the second-stage processing unit performs the correction process as the quality improvement process. In addition, the result of the correction process which is the result of the quality improvement process of the second-stage processing unit is input to the third-stage processing unit and the third-stage processing unit performs the correction process as the quality improvement process. 
     In Step S 209 , the image processing apparatus determines whether there is a next processing stage. 
     In a case in which there is a next processing stage, the image processing apparatus proceeds to Step S 210 . 
     In Step S 210 , the corrected image is output to the processing unit in the next stage and the correction unit in the next stage starts a process. 
     That is, the processing unit in the next stage performs the process in Step S 203  and the subsequent steps. 
     In a case in which it is determined in Step S 209  that there is no next processing stage, the image processing apparatus ends the process. 
     As such, the image processing apparatus according to the present disclosure corrects the pixel value using a multi-stage process (cascade process) as described with reference to the drawings including  FIG. 6 . 
     This configuration makes it possible for the processing units in each stage to use accurate correction coefficients corresponding to a larger number of classes. 
     As described above, the correction coefficients are correction coefficients subjected to class classification in accordance with the learning process which is performed in advance. The correction coefficients can be accurate correction coefficients corresponding to a larger number of classes, that is, the optimum correction coefficients corresponding to the image feature amounts. 
     As described with reference to  FIG. 13 , for example, in a case in which the number of classes associated with the correction coefficients that can be used in the correction processing units in three stages, that is the first to third stage illustrated in  FIG. 13  is 1000, in three-stage correction processes in the first to third stages, correction can be performed using different correction coefficients corresponding to 1000×1000×1000=1000000000, that is, 1K×1K×1K=1G classes. 
     Correction using accurate correction coefficients corresponding to finely classified image characteristics is achieved by this configuration. 
     In addition, in the tap selection process, the tap can be selected in three stages of the first-stage to third-stage tap selection units  214  to  234 . 
     The tap is a reference pixel range that is applied to calculate the corrected pixel value. As described above, for example, the setting of selecting the optimum tap (reference pixel range) in accordance with the feature amounts in the vicinity of the correction pixel can be performed. 
     The tap selection is performed in three times to select the optimum tap corresponding to the feature amount detected in each stage. 
     In the image processing apparatus according to the present disclosure, optimal correction corresponding to the feature amount of each local region of the image is achieved by this process. 
     8. FOR EXAMPLE OF HARDWARE CONFIGURATION OF IMAGE PROCESSING APPARATUS 
     Next, an example of the hardware configuration of the image processing apparatus will be described with reference to  FIG. 18 . 
       FIG. 18  is a diagram illustrating an example of the hardware configuration of the image processing apparatus that performs the process according to the present disclosure. 
     A central processing unit (CPU)  501  functions as a control unit or a data processing unit that performs various processes in accordance with a program stored in a read only memory (ROM)  502  or a storage unit  508 . For example, the CPU  501  performs the process according to the sequence described in the above-mentioned embodiment. A random access memory (RAM)  503  stores, for example, programs or data executed by the CPU  501 . The CPU  501 , the ROM  502 , and the RAM  503  are connected to each other by a bus  504 . 
     The CPU  501  is connected to an input/output interface  505  through the bus  504 . An input unit  506  that inputs an image captured by an imaging unit  521  and includes various switches, a keyboard, a mouse, and a microphone which can be used by the user to input information and an output unit  507  that outputs data to, for example, a display unit  522  or a speaker are connected to the input/output interface  505 . The CPU  501  performs various processes in response to commands input from the input unit  506  and outputs the processing results to, for example, the output unit  507 . 
     The storage unit  508  connected to the input/output interface  505  is, for example, a hard disk drive and stores the programs or various types of data executed by the CPU  501 . A communication unit  509  functions as a transmitting and receiving unit for Wi-Fi communication, Bluetooth (registered trademark) (BT) communication, and other types of data communication through a network, such as the Internet or a local area network, and communicates with external apparatuses. 
     A drive  510  connected to the input/output interface  505  drives a removable medium  511 , such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory such as a memory card, to record or read data. 
     9. FOR APPLICATION EXAMPLES OF IMAGE PROCESSING APPARATUS ACCORDING TO PRESENT DISCLOSURE 
     The technology according to the present disclosure can be applied to various products. For example, the technology according to the present disclosure may be implemented as an apparatus provided in any type of moving object such as a vehicle, an electric vehicle, a hybrid electric vehicle, a motorcycle, a bicycle, a personal mobility, an airplane, a drone, a ship, a robot, a construction machine, and an agricultural machine (tractor). 
       FIG. 19  is a block diagram illustrating an example of the schematic configuration of a vehicle control system  7000  that is an example of a moving object control system to which the technology according to the present disclosure can be applied. The vehicle control system  7000  includes a plurality of electronic control units connected through a communication network  7010 . In the example illustrated in  FIG. 19 , the vehicle control system  7000  includes a driving system control unit  7100 , a body system control unit  7200 , a battery control unit  7300 , an out-of-vehicle information detection unit  7400 , an in-vehicle information detection unit  7500 , and an overall control unit  7600 . The communication network  7010  connecting the plurality of control units may be an in-vehicle communication network based on any standard, such as a controller area network (CAN), a local interconnect network (LIN), a local area network (LAN), or FlexRay (registered trademark). 
     Each control unit includes a microcomputer that performs an arithmetic process in accordance with various programs, a storage unit that stores, for example, programs executed by the microcomputer or parameters used for various arithmetic operations, and a driving circuit that drives various apparatuses to be controlled. Each control unit includes a network I/F for communication with other control units through the communication network  7010  and a communication I/F for wired communication or wireless communication with apparatuses or sensors inside or outside the vehicle. In  FIG. 19 , a microcomputer  7610 , a general-purpose communication I/F  7620 , a private communication I/F  7630 , a positioning unit  7640 , a beacon receiving unit  7650 , an in-vehicle device I/F  7660 , an audio and image output unit  7670 , an in-vehicle network I/F  7680 , and a storage unit  7690  are illustrated as the functional configurations of the overall control unit  7600 . Similarly, the other control units include, for example, a microcomputer, a communication I/F, and a storage unit. 
     The driving system control unit  7100  controls the operation of devices related to a vehicle driving system in accordance with various programs. For example, the driving system control unit  7100  functions as a control device for a driving force generation device for generating the driving force of the vehicle, such as an internal combustion engine or a driving motor, a driving force transmission mechanism for transmitting the driving force to wheels, a steering mechanism for adjusting the steering of the vehicle, and a braking device for generating the braking force of the vehicle. The driving system control unit  7100  may function as a control device for an antilock brake system (ABS) or an electronic stability control (ESC) device. 
     A vehicle state detection unit  7110  is connected to the driving system control unit  7100 . The vehicle state detection unit  7110  may include, for example, at least one of a gyro sensor that detects an angular velocity in the axial rotational motion of a vehicle body, an acceleration sensor that detects the acceleration of the vehicle, or a sensor for detecting the operation amount of an accelerator pedal, the operation amount of a brake pedal, the steering angle of a steering wheel, an engine speed, or the rotational speed of the wheels. The driving system control unit  7100  performs an arithmetic process using a signal input from the vehicle state detection unit  7110  to control, for example, the internal combustion engine, the driving motor, an electric power steering device, or the braking device. 
     The body system control unit  7200  controls the operation of various devices provided in the vehicle body in accordance with various programs. For example, the body system control unit  7200  functions as a control device for a keyless entry system, a smart key system, a power window device, and various lamps such as a head lamp, a back lamp, a brake lamp, a blinker, and a fog lamp. In this case, the body system control unit  7200  may receive radio waves transmitted from a portable device substituting a key or signals from various switches. The body system control unit  7200  receives the input radio waves or signals and controls, for example, a door lock device, a power window device, and lamps of the vehicle. 
     The battery control unit  7300  controls a secondary battery  7310  which is a power supply source of the driving motor in accordance with various programs. Information, such as a battery temperature, a battery output voltage, or the remaining capacity of the battery, is input from a battery device provided with the secondary battery  7310  to the battery control unit  7300 . The battery control unit  7300  performs an arithmetic process using these signals to perform temperature adjustment control for the secondary battery  7310  or to control, for example, a cooling device provided in the battery device. 
     The out-of-vehicle information detection unit  7400  detects information outside the vehicle provided with vehicle control system  7000 . For example, at least one of an imaging unit  7410  or the out-of-vehicle information detector  7420  is connected to the out-of-vehicle information detection unit  7400 . The imaging unit  7410  includes at least one of a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, or other cameras. The out-of-vehicle information detector  7420  includes at least one of an environment sensor for detecting the current weather or climate or a surrounding information detection sensor for detecting other vehicles, obstacles or pedestrians around the vehicle provided with the vehicle control system  7000 . 
     The environment sensor may be, for example, at least one of a raindrop sensor that detects wet weather, a fog sensor that detects fog, a sunshine sensor that detects sunshine intensity, or a snow sensor that detects snowfall. The surrounding information detection sensor may be at least one of an ultrasonic sensor, a radar device, or a light detection and ranging or laser imaging detection and ranging (LIDAR) device. The imaging unit  7410  and the out-of-vehicle information detector  7420  may be provided as independent sensors or devices or may be provided as devices into which a plurality of sensors or devices are integrated. 
     Here,  FIG. 20  illustrates an example of the installation position of the imaging unit  7410  and the out-of-vehicle information detector  7420 . Imaging units  7910 ,  7912 ,  7914 ,  7916 , and  7918  are provided, for example, in least one of a front nose, a side mirror, a rear bumper, a back door, or an inner upper part of a windshield of a vehicle  7900 . The imaging unit  7910  provided in the front nose and the imaging unit  7918  provided in the inner upper part of the windshield of the vehicle mainly acquire images in front of the vehicle  7900 . The imaging units  7912  and  7914  provided in the side mirrors main acquire images on the side of the vehicle  7900 . The imaging unit  7916  provided in the rear bumper or the back door mainly acquires an image behind the vehicle  7900 . The imaging unit  7918  provided in the inner upper part of the windshield of the vehicle is mainly used to detect, for example, vehicles in front, pedestrians, obstacles, traffic lights, traffic signs, and lanes. 
     It should be noted that  FIG. 20  illustrates an example of the imaging range of each of the imaging units  7910 ,  7912 ,  7914 , and  7916 . An imaging range a indicates the imaging range of the imaging unit  7910  provided in the front nose, imaging ranges b and c indicate the imaging ranges of the imaging units  7912  and  7914  provided in the side mirrors, respectively, and an imaging range d indicates the imaging range of the imaging unit  7916  provided in the rear bumper or the back door. For example, image data captured by the imaging units  7910 ,  7912 ,  7914 , and  7916  is superimposed to obtain a bird&#39;s-eye view image of the vehicle  7900 . 
     Out-of-vehicle information detection units  7920 ,  7922 ,  7924 ,  7926 ,  7928 , and  7930  provided on the front, rear, side, and corners of the vehicle  7900  and in the upper part of the windshield in the vehicle may be, for example, ultrasonic sensors or radar devices. The out-of-vehicle information detection units  7920 ,  7926 , and  7930  provided in the front nose, the rear bumper, and the back door of the vehicle  7900  and in the upper part of the windshield in the vehicle may be, for example, LIDAR devices. These out-of-vehicle information detection units  7920  to  7930  are mainly used to detect, for example, vehicles in front, pedestrians, and obstacles. 
     Returning to  FIG. 19 , the description will be continued. The out-of-vehicle information detection unit  7400  directs the imaging unit  7410  to capture an image outside the vehicle and receives the captured image data. In addition, the out-of-vehicle information detection unit  7400  receives information output from the out-of-vehicle information detector  7420  connected thereto. In a case in which the out-of-vehicle information detector  7420  is an ultrasonic sensor, a radar device, or a LIDAR device, the out-of-vehicle information detection unit  7400  transmits, for example, ultrasonic waves or radio waves and receives information of received reflected waves. The out-of-vehicle information detection unit  7400  may perform an object detection process or a distance detection process for, for example, persons, vehicles, obstacles, signs, and characters on a road surface on the basis of the received information. The out-of-vehicle information detection unit  7400  may perform an environment recognition process for recognizing, for example, rainfall, fog, and road surface conditions on the basis of the received information. The out-of-vehicle information detection unit  7400  may calculate the distance to an object outside the vehicle on the basis of the received information. 
     Further, the out-of-vehicle information detection unit  7400  may perform an image recognition process or a distance detection process that recognizes, for example, persons, vehicles, obstacles, signs, and characters on a road surface on the basis of the received image data. The out-of-vehicle information detection unit  7400  may perform a process, such as distortion correction or positioning, for the received image data and may combine the image data captured by different imaging units  7410  to generate a bird&#39;s eye view image or a panoramic image. The out-of-vehicle information detection unit  7400  may perform a viewpoint conversion process using the image data captured by different imaging units  7410 . 
     The in-vehicle information detection unit  7500  detects information in the vehicle. For example, a driver state detection unit  7510  that detects the state of a driver is connected to the in-vehicle information detection unit  7500 . The driver state detection unit  7510  may include, for example, a camera that captures an image of the driver, a biological sensor that detects the biological information of the driver, and a microphone that collects sound in the vehicle. The biological sensor is provided, for example, on the surface of a seat or a steering wheel and detects the biological information of a passenger sitting on the seat or the driver who grips the steering wheel. The in-vehicle information detection unit  7500  may calculate the degree of fatigue or concentration of the driver on the basis of the detection information input from the driver state detection unit  7510  or may determine whether the driver falls sleep. The in-vehicle information detection unit  7500  may perform a process, such as a noise canceling process, for the collected audio signal. 
     The overall control unit  7600  controls the overall operation of the vehicle control system  7000  in accordance with various programs. An input unit  7800  is connected to the overall control unit  7600 . The input unit  7800  is implemented by, for example, a device that can be operated to input information by the passenger, such as a touch panel, a button, a microphone, a switch, or a lever. For example, data obtained by voice recognition for voice input by the microphone may be input to the overall control unit  7600 . The input unit  7800  may be, for example, a remote control device using infrared rays or other radio waves or an external connection device, such as a mobile phone or a personal digital assistant (PDA) corresponding to the operation of the vehicle control system  7000 . The input unit  7800  may be, for example, a camera. In this case, the passenger can input information by gesture. Alternatively, data obtained by detecting the movement of a wearable device worn by the passenger may be input. In addition, the input unit  7800  may include, for example, an input control circuit that generates an input signal on the basis of information input by the passenger through the input unit  7800  and outputs the generated signal to the overall control unit  7600 . For example, the passenger operates the input unit  7800  to input various types of data to the vehicle control system  7000  or to instruct a processing operation. 
     The storage unit  7690  may include a read only memory (ROM) that stores various program executed by a microcomputer and a random access memory (RAM) that stores, for example, various parameters, the result of computation, and sensor values. In addition, the storage unit  7690  may be implemented by a magnetic storage device, such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, or a magneto-optical storage device. 
     The general-purpose communication I/F  7620  is a general-purpose communication I/F that relays communication with various apparatuses in an external environment  7750 . The general-purpose communication I/F  7620  may be implemented by a cellular communication protocol, such as Global System of Mobile communications (GSM) (registered trademark), WiMAX, long term evolution (LTE), or LTE-advanced (LTE-A), or other wireless communication protocols, such as a wireless LAN (also referred to as Wi-Fi (registered trademark)) and Bluetooth (registered trademark). The general-purpose communication I/F  7620  may be connected to an apparatus (for example, an application server or a control server) on an external network (for example, the Internet, a cloud network, or an operator-specific network) through, for example, a base station or an access point. In addition, the general-purpose communication I/F  7620  may be connected to a terminal (for example, a terminal of a driver, a pedestrian, or a shop, or a machine type communication (MTC) terminal) in the vicinity of the vehicle by, for example, a peer-to-peer (P2P) technology. 
     The private communication I/F  7630  is a communication I/F that supports a communication protocol designed for use in vehicles. The private communication I/F  7630  may be implemented by a standard protocol, such as wireless access in vehicle environment (WAVE) or dedicated short range communications (DSRC) that is a combination of IEEE 802.11p which is a lower layer and IEEE1609 which is an upper layer, or a cellular communication protocol. The private communication I/F  7630  typically performs V2X communication which is a concept including at least one of vehicle-to-vehicle communication, vehicle-to-infrastructure communication, vehicle-to-home communication, or vehicle-to-pedestrian communication. 
     The positioning unit  7640  receives, for example, a global navigation satellite system (GNSS) signal (for example, a global positioning system (GPS) signal from a GPS satellite) from a GNSS satellite, performs positioning, and generates positional information including the latitude, longitude, and altitude of the vehicle. It should be noted that the positioning unit  7640  may specify the current position by exchanging signals with a wireless access point or may acquire positional information from a terminal having a positioning function, such as a mobile phone, a PHS, or a smart phone. 
     The beacon receiving unit  7650  receives, for example, radio waves or electromagnetic waves transmitted from a wireless station installed on a road and acquires information, such as the current position, traffic jams, closure, or the time required. It should be noted that, the functions of the beacon receiving unit  7650  may be included in the private communication I/F  7630 . 
     The in-vehicle device I/F  7660  is a communication interface that relays the connection between the microcomputer  7610  and various in-vehicle devices  7760  provided in the vehicle. The in-vehicle device I/F  7660  may establish a wireless connection using a wireless communication protocol, such as a wireless LAN, Bluetooth (registered trademark), near field communication (NFC), or wireless USB (WUSB). In addition, the in-vehicle device I/F  7660  may establish a wired connection, such as universal serial bus (USB), High-Definition Multimedia Interface (HDMI) (registered trademark), or mobile high-definition link (MHL), through a connection terminal (not illustrated) (and a cable if necessary). The in-vehicle device  7760  may include, for example, at least one of a mobile device or a wearable device of a passenger, or an information device carried in or attached to the vehicle. In addition, the in-vehicle device  7760  may include a navigation device that performs a route search to any destination. The in-vehicle device  7760  exchanges control signals or data signals with these in-vehicle devices  7760 . 
     The in-vehicle network I/F  7680  is an interface that relays communication between the microcomputer  7610  and the communication network  7010 . The in-vehicle network I/F  7680  transmits and receives, for example, signals in accordance with a predetermined protocol supported by the communication network  7010 . 
     The microcomputer  7610  of the overall control unit  7600  controls the vehicle control system  7000  in accordance with various programs on the basis of the information acquired through at least one of the general-purpose communication I/F  7620 , the private communication I/F  7630 , the positioning unit  7640 , the beacon receiving unit  7650 , the in-vehicle device I/F  7660 , or the in-vehicle network I/F  7680 . For example, the microcomputer  7610  may calculate a control target value of the driving force generation device, the steering mechanism, or the braking device on the basis of the acquired information inside and outside the vehicle and may output a control command to the driving system control unit  7100 . For example, the microcomputer  7610  may perform cooperative control for achieving the function of an advanced driver assistance system (ADAS) including, for example, collision avoidance or shock mitigation of a vehicle, follow-up traveling based on an inter-vehicle distance, vehicle speed maintenance traveling, vehicle collision warning, or vehicle lane departure warning. In addition, the microcomputer  7610  may control, for example, the driving force generation device, the steering mechanism, or the braking device on the basis of the acquired information related to the surroundings of the vehicle to perform cooperative control for the purpose of automatic driving for autonomous driving without depending on the driver&#39;s operation. 
     The microcomputer  7610  may generate three-dimensional distance information between the vehicle and an object, such as the surrounding structure or a person, on the basis of the information acquired through at least one of the general-purpose communication I/F  7620 , the private communication I/F  7630 , the positioning unit  7640 , the beacon receiving unit  7650 , the in-vehicle device I/F  7660 , or the in-vehicle network I/F  7680  and generate local map information including information around the current position of the vehicle. In addition, the microcomputer  7610  may predict a danger, such as the collision of a vehicle, the approach of a pedestrian, or entrance to a closed road, and generate a warning signal on the basis of the acquired information. The warning signal may be, for example, a signal for generating a warning sound or for turning on a warning lamp. 
     The audio and image output unit  7670  transmits an output signal of at least one of audio or images to an output device that can visually or aurally notify information to a passenger of the vehicle or the outside of the vehicle. In the example illustrated in  FIG. 19 , an audio speaker  7710 , a display unit  7720 , and an instrument panel  7730  are illustrated as the output device. The display unit  7720  may include, for example, at least one of an on-board display or a head-up display. The display unit  7720  may have an augmented reality (AR) display function. The output device may be other devices including a headphone, a wearable device worn by a passenger, such as a glasses-type display, a projector, and a lamp in addition to these devices. In a case in which the output device is a display device, the display device visually displays the results obtained by various processes performed by the microcomputer  7610  or the information received from other control units in various formats, such as text, an image, a table, and a graph. In addition, in a case in which the output device is an audio output device, the audio output device converts an audio signal including the reproduced audio data or acoustic data into an analog signal and aurally outputs the analog signal. 
     It should be noted that, in the example illustrated in  FIG. 19 , at least two control units connected through the communication network  7010  may be integrated into one control unit. Alternatively, each control unit may be configured by a plurality of control units. In addition, the vehicle control system  7000  may include other control units (not illustrated). Further, in the above description, some or all of the functions of any control unit may be provided in other control units. That is, as long as information is transmitted and received through the communication network  7010 , a predetermined arithmetic process may be performed by any control unit. Similarly, a sensor or a device connected to any control unit may be connected to other control units and a plurality of control units may mutually transmit and receive detection information through the communication network  7010 . 
     It should be noted that a computer program for implementing each function of the image processing apparatus according to the above-described embodiment can be implemented in any control unit. In addition, a computer-readable recording medium having the computer program stored therein may be provided. The recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, or a flash memory. Further, the computer program may be distributed through, for example, network, without using the recording medium. 
     In the vehicle control system  7000  described above, the image processing apparatus according to the above-described embodiment can be applied to the overall control unit  7600  according to the application example illustrated in  FIG. 19 . For example, a CPU  801  of the image processing apparatus illustrated in  FIG. 20  corresponds to the microcomputer  7610  of the overall control unit  7600  illustrated in  FIG. 19 , a ROM  802 , a RAM  803 , and a storage unit  808  of the image processing apparatus illustrated in  FIG. 20  correspond to the storage unit  7690  of the overall control unit  7600  illustrated in  FIG. 19 , and a communication unit  809  of the image processing apparatus illustrated in  FIG. 20  corresponds to the in-vehicle network I/F  7680  of the overall control unit  7600  illustrated in  FIG. 19 . 
     In addition, at least some of the components of the above-mentioned image processing apparatus may be implemented in a module (for example, an integrated circuit module configured by one die) for the overall control unit  7600  illustrated in  FIG. 19 . Alternatively, the above-mentioned image processing apparatus may be implemented by a plurality of control units of the vehicle control system  7000  illustrated in  FIG. 19 . 
     10. SUMMARY OF CONFIGURATION OF PRESENT DISCLOSURE 
     The embodiments of the present disclosure have been described in detail above with reference to the specific embodiments. However, it is obvious that those skilled in the art can make modifications and substitutions of the embodiments without departing from the scope and spirit of the present disclosure. That is, the invention has been disclosed in the form of illustration and should not be construed as being limited to the embodiments. The claims need be referred to in order to determine the scope of the present disclosure. 
     It should be noted that the technology disclosed in the specification can have the following configuration. 
     (1) An image processing apparatus including: 
     an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units, in which 
     the image correction unit inputs a low-quality image which is an image to be corrected and a high-quality image which is a reference image, 
     each of the plurality of processing units in each stage performs a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image, and 
     the class correspondence correction coefficient is generated by a learning process. 
     (2) The image processing apparatus according to (1), in which 
     among the plurality of processing units in each stage, a processing unit in a previous stage performs the correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from a degraded image of the high-quality image which has a higher degradation level than that in a processing unit in a subsequent stage. 
     (3) The image processing apparatus according to (1) or (2), in which 
     the first-stage processing unit performs the correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from a degraded image of the high-quality image having a degradation level that is substantially equal to a degradation level of the low-quality image which is the image to be corrected. 
     (4) The image processing apparatus according to any one of (1) to (3), in which 
     the class correspondence correction coefficient is generated by the learning process which is performed in advance on the basis of a sample image. 
     (5) The image processing apparatus according to (4), in which 
     the class correspondence correction coefficient corresponds to a feature amount extracted from the sample image or a degraded image of the sample image, and 
     among the plurality of processing units in each stage, the processing unit in the previous stage performs the correction process, using a class correspondence correction coefficient corresponding to a feature amount extracted from an image which has a higher degradation level than that in the processing unit in the subsequent stage. 
     (6) The image processing apparatus according to any one of (1) to (5), in which 
     among the plurality of processing units in each stage, the processing unit in the previous stage performs the correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from an image which has a lower resolution than that in the processing unit in the subsequent stage. 
     (7) The image processing apparatus according to any one of (1) to (6), in which 
     the class correspondence correction coefficient is a correction coefficient associated with a set class based on a combination of a feature amount of an image with a high degradation level and a feature amount of an image with a low degradation level. 
     (8) The image processing apparatus according to any one of (1) to (7), in which 
     the class correspondence correction coefficient includes a multiplication coefficient corresponding to a reference pixel used in a filter that calculates a value of a pixel to be corrected. 
     (9) The image processing apparatus according to any one of (1) to (8), in which 
     each of the plurality of processing units in each stage includes a tap selection unit that sets a reference pixel range which is referred to in a case in which a correction value of the pixel to be corrected in the low-quality image which is the image to be corrected is calculated. 
     (10) The image processing apparatus according to (9), in which 
     the tap selection unit sets the reference pixel range in accordance with a feature amount of a pixel region including the pixel to be corrected. 
     (11) The image processing apparatus according to any one of (1) to (10), in which 
     the feature amount is any one of: 
     (a) brightness distribution information; 
     (b) blurred state information; and 
     (c) noise information. 
     (12) The image processing apparatus according to any one of (1) to (11), in which 
     the low-quality image which is the image to be corrected is a far-infrared image or a fluorescent image. 
     (13) The image processing apparatus according to any one of (1) to (12), in which 
     the high-quality image which is the reference image is a visible image. 
     (14) The image processing apparatus according to any one of (1) to (13) further including: 
     a high-quality imaging unit that performs a process of capturing the visible image; and 
     a low-quality imaging unit that performs a process of capturing the far-infrared image or the fluorescent image, in which 
     the image correction unit receives a high-quality image and a low-quality image captured by the high-quality imaging unit and the low-quality imaging unit and performs the correction process for the low-quality image. 
     (15) An image processing method to be performed in an image processing apparatus including an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units, the method including: 
     an image input step of allowing the image correction unit to input a low-quality image which is an image to be corrected and a high-quality image which is a reference image; and 
     a correction step of allowing each of the plurality of processing units in each stage to perform a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image, in which 
     the class correspondence correction coefficient used in the correction step is generated by a learning process. 
     (16) A program that causes an image processing apparatus including an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages which include first-stage to final-stage processing units to perform image processing, in which 
     the program causes the image correction unit to perform an image input step of inputting a low-quality image which is an image to be corrected and a high-quality image which is a reference image and causes each of the plurality of processing units in each stage to perform a correction step of performing a correction process for the low-quality image, using a class correspondence correction coefficient classified in accordance with a class corresponding to a feature amount extracted from the high-quality image or a degraded image of the high-quality image, and 
     the class correspondence correction coefficient used in the correction step is generated by a learning process. 
     In addition, a series of processes described in the specification may be implemented by hardware, software, or a combination thereof. In a case in which the processes are implemented by software, a program having a processing sequence recorded thereon may be installed in a memory of a computer incorporated into dedicated hardware and then executed, or the program may be installed in a general-purpose computer capable of performing various processes and then executed. For example, the program may be recorded on a recording medium in advance. The program may be installed from the recording medium to the computer. Alternatively, the program may be received by the computer through a network, such as a local area network (LAN) or the Internet, and then installed in a recording medium, such as a hard disk drive, provided in the computer. 
     It should be noted that the various processes described in the specification are not only performed in time series in accordance with the description, but also may be performed in parallel or individually in accordance with the processing capability of the apparatus performing the processes or if needed. Further, in the specification, the system is a logical set configuration of a plurality of apparatuses and is not limited to the configuration in which the apparatuses are provided in the same housing. 
     INDUSTRIAL APPLICABILITY 
     As described above, according to the configuration of an embodiment of the present disclosure, an apparatus and a method that perform a process of improving the quality of a low-quality image, such as a far-infrared image, are achieved. 
     Specifically, for example, the apparatus includes an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages. The image correction unit inputs a low-quality image which is an image to be corrected and a high-quality image which is a reference image. Each of the processing units in each stage performs a correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from a degraded image of the high-quality image. A processing unit in a previous stage performs the correction process, using a class correspondence correction coefficient corresponding to a feature amount extracted from an image having a higher degradation level than that in a processing unit in a subsequent stage. The class correspondence correction coefficient is generated by a learning process. 
     An apparatus and a method that perform a process of improving the quality of a low-quality image, such as a far-infrared image, are achieved by these processes. 
     REFERENCE SIGNS LIST 
     
         
           10  living body tissue 
           11  blood vessel 
           20  visible image 
           21  infrared image 
           100  image processing apparatus 
           101  control unit 
           102  storage unit 
           103  codec 
           104  input unit 
           105  output unit 
           106  imaging unit 
           107  high-quality imaging unit 
           108  low-quality imaging unit 
           111  first imaging element 
           112  second imaging element 
           120  image processing unit 
           121  scaler 
           122  disparity amount and movement amount detection unit 
           123  image positioning unit 
           127  image correction unit 
           151  high-quality image 
           152  low-quality image 
           161  positioned high-quality image 
           162  positioned low-quality image 
           172  high-quality corrected image 
           211 ,  221 ,  231 ,  411  degradation-simulated image generation unit 
           212 ,  222 ,  232 ,  412  class classification processing unit 
           213 ,  223 ,  233 ,  413  class correspondence correction coefficient storage unit 
           214 ,  224 ,  234 ,  414  tap selection unit 
           215 ,  225 ,  235 ,  415  image correction unit 
           501  CPU 
           502  ROM 
           503  RAM 
           504  bus 
           505  input/output interface 
           506  input unit 
           507  output unit 
           508  storage unit 
           509  communication unit 
           510  drive 
           511  removable medium 
           521  imaging unit 
           522  display unit