JP2020003885A - Image processing apparatus, image processing method, and program - Google Patents

Abstract

To provide an image processing apparatus, an image processing method, and a program that can obtain a result of fluctuation correction with less artifact even when fluctuation intensity is high or sensor noise is strong.SOLUTION: An information processing apparatus includes output means for outputting a first correction result of a plurality of image data by a first correction process and a second correction result of the plurality of image data obtained by a second correction process different from the first correction process based on an influence degree of fluctuation of the plurality of image data having image quality deteriorated by influence of the atmospheric fluctuation.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a method for reducing the effects of atmospheric turbulence in an image.

  2. Description of the Related Art Conventionally, in a surveillance camera system and the like, a technique for correcting image degradation due to atmospheric turbulence (hereinafter referred to as fluctuation correction) has been used. Here, atmospheric fluctuation refers to a natural phenomenon in which the refractive index changes spatially and temporally due to atmospheric turbulence and temperature differences. Atmospheric fluctuations are also called heat haze. Atmospheric fluctuations cause variations in the refraction and phase of light, and as a result, blurring and distortion (hereinafter referred to as fluctuations) that temporally change occur in an image of a subject, particularly when a distant image is taken with a telephoto lens. It is desirable that such deterioration of the image be removed because it greatly reduces the visibility of the subject.

  In order to solve such a problem, speckle imaging is known as a technique for generating an image in which blur and distortion are simultaneously reduced from images of a plurality of frames. In the following description, the image data that forms a moving image and is arranged in time series is called a frame. Speckle imaging is a technique originally developed in the field of astronomy, and independently estimates the amplitude component and the phase component of the Fourier spectrum of a reconstructed image. The amplitude component is estimated by calculating the average of the power spectrum of the input frame and applying a filter to amplify the high-frequency component determined based on the optical transfer function of the optical system of the imaging apparatus. On the other hand, as for the phase component, the phase value of the high-frequency component is sequentially estimated using the known phase value of the low-frequency component based on the bispectrum of the input image. Patent Literature 1 discloses a method of applying speckle imaging to fluctuation correction.

U.S. Pat. No. 7,245,742

  However, the prior art has a problem that the phase estimation accuracy is reduced when the fluctuation intensity is high or the sensor noise is strong, and as a result, an artifact specific to the reconstructed image is generated, and the visibility is reduced.

  In order to solve the above-described problem, the image processing apparatus according to the present invention is configured such that the plurality of images obtained by the first correction process are based on the influence of the fluctuation of a plurality of image data whose image quality has deteriorated due to the fluctuation of the atmosphere. Output means for outputting a first correction result of the data and a second correction result of the plurality of image data by a second correction process different from the first correction process.

  Even when the fluctuation intensity is high or the sensor noise is high, it is possible to obtain a fluctuation correction result with less artifact.

FIG. 1 is a schematic diagram illustrating a configuration of an apparatus according to a first embodiment. FIG. 2 is a block diagram illustrating a configuration of the information processing apparatus according to the first embodiment. FIG. 2 is a block diagram illustrating a configuration of a parameter calculation unit according to the first embodiment. FIG. 9 is a block diagram illustrating a configuration of a parameter calculation unit according to the second embodiment. FIG. 3 is a block diagram illustrating a configuration of a first correction unit according to the first embodiment. 5 is a flowchart illustrating a flow of a process performed by the information processing apparatus according to the first embodiment. 5 is a flowchart illustrating a flow of a process performed by a parameter calculation unit according to the first embodiment. 9 is a flowchart illustrating a flow of a process performed by a first correction unit according to the first embodiment. 9 is a flowchart illustrating a flow of a process performed by a parameter calculation unit according to the second embodiment. FIG. 9 is a block diagram illustrating a configuration of an information processing apparatus according to a third embodiment. 13 is a flowchart illustrating a flow of a process performed by the information processing apparatus according to the third embodiment. FIG. 13 is a block diagram illustrating a configuration of a parameter calculation unit according to a fourth embodiment. 15 is a flowchart illustrating the flow of a process performed by a parameter calculation unit according to the fourth embodiment. FIG. 2 is another block diagram illustrating the configuration of the information processing apparatus according to the first embodiment.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the following embodiments do not limit the present invention, and not all combinations of the features described in the present embodiments are necessarily essential to the solution of the present invention. The same components will be described with the same reference numerals.

  In the present embodiment, an example will be described in which a non-uniformity (variation) of an in-plane displacement amount is calculated from input image data as a parameter indicating the intensity of atmospheric fluctuation, thereby achieving good fluctuation correction. First, the configuration of the information processing apparatus (image processing apparatus) according to the present embodiment will be described.

  FIG. 1 is a diagram illustrating an example of the configuration of the information processing apparatus according to the present embodiment. The information processing apparatus 100 according to the present embodiment includes a CPU 101, a RAM 102, a ROM 103, a secondary storage device 104, an input interface 105, and an output interface 106. The components of the information processing apparatus 100 are mutually connected by a system bus 107. Further, the information processing apparatus 100 is connected to the external storage device 108 and the operation unit 110 via the input interface 105. Further, the information processing apparatus 100 is connected to an external storage device 108 and a display device 109 via an output interface 106.

  The CPU 101 executes a program stored in the ROM 103 using the RAM 102 as a work memory, and comprehensively controls each component of the information processing apparatus 100 via the system bus 107. Thereby, various processes described later are executed. The secondary storage device 104 is a storage device that stores various data handled by the information processing device 100, and in this embodiment, an HDD is used. The CPU 101 writes data into the secondary storage device 104 via the system bus 107 and reads data stored in the secondary storage device 104. Various storage devices such as an optical disk drive and a flash memory can be used for the secondary storage device 104 in addition to the HDD.

  The input interface 105 is, for example, a serial bus interface such as USB or IEEE1394. The information processing apparatus 100 inputs data, commands, and the like from an external device via the input interface 105. In the present embodiment, the information processing apparatus 100 acquires data from the external storage device 108 (for example, a storage medium such as a hard disk, a memory card, a CF card, an SD card, or a USB memory) via the input interface 105. In the present embodiment, the information processing apparatus 100 acquires a user instruction input to the operation unit 110 via the input interface 105. The operation unit 110 is an input device such as a mouse or a keyboard, and inputs a user's instruction.

  The output interface 106 is a serial bus interface such as USB or IEEE1394, like the input interface 105. The output interface 106 may be a video output terminal such as DVI or HDMI (registered trademark). The information processing device 100 outputs data and the like to an external device via the output interface 106. In this embodiment, the information processing apparatus 100 outputs data (for example, image data) processed by the CPU 101 to the display device 109 (various image display devices such as a liquid crystal display) via the output interface 106. Although the components of the information processing apparatus 100 are present in addition to those described above, they are not the main focus of the present embodiment, and a description thereof will be omitted.

  FIG. 2 is a functional block diagram of the information processing apparatus according to the present embodiment. As shown in FIG. 2, the information processing apparatus 100 includes an image data acquisition unit 201, an image division unit 202, a parameter calculation unit 203, a phase estimation accuracy determination unit 204, a first correction unit 205, a second correction unit 206, It has a function as the image combining unit 207.

  Further, in this embodiment, the parameter calculation unit 203 has functions as a motion vector calculation unit 301 and a variation 302 unit as shown in FIG.

  As shown in FIG. 5, the first correction unit 205 includes a preprocessing unit 501, a Fourier transform unit 502, a bispectrum calculation unit 503, a phase estimation unit 504, an average power spectrum calculation unit 505, an amplitude estimation unit 506, It has a function as an image synthesizing unit 507.

  The functions of the above components are realized by the CPU 101 reading and executing a control program stored in the ROM 103. The information processing device 100 may be configured to include a dedicated processing circuit corresponding to each unit. Hereinafter, the flow of processing performed by each unit will be described.

  Hereinafter, the flow of processing performed by each unit will be described. As a method of performing the fluctuation correction, a “speckle imaging process” is known. This processing is compared with other known processing (for example, "blind deconvolution processing" or "averaging processing (averaging the preceding and following frames, and filtering to enhance the edge)"). Can be satisfactorily corrected. However, when the fluctuation intensity is high or the sensor noise is high, the phase estimation accuracy is reduced, and as a result, an artifact peculiar to the reconstructed image is generated and visibility is reduced. Therefore, in this embodiment, when it is determined that the phase estimation accuracy is high, the fluctuation is corrected by the speckle imaging process, and when not, other processes (for example, “blind deconvolution process”, “averaging process”, etc.) ) To perform the fluctuation correction.

  FIG. 6 is a flowchart illustrating the flow of the process according to the present embodiment.

  In step S601, the image data obtaining unit 201 obtains image data including a plurality of frames to be processed.

  In step S602, the image dividing unit 202 divides the image data into a plurality of small area data (block data). Here, duplication of the plurality of small area data is allowed. The size of the small area data is preferably smaller than a range (isoplanatic patch) in which the influence of atmospheric turbulence can be considered to be constant. Subsequent steps S603, S604, and S605a / b are performed for each of the small area data in block units.

  In step S603, the parameter calculation unit 203 calculates a parameter indicating the intensity of atmospheric turbulence at the time of acquiring the image data. Details of the processing will be described later.

  In step S604, the phase estimation accuracy determination unit 204 determines the phase estimation accuracy in speckle imaging processing described later. When the parameter calculated in step S603 is smaller than the threshold value calculated in advance, it is determined that the phase estimation accuracy is high, and the process proceeds to step S605a. When the parameter calculated in step S603 is larger than the threshold calculated in advance, it is determined that the phase estimation accuracy is low, and the process proceeds to step S605b.

  In step S605a, the first correction unit generates small area data in which fluctuation has been corrected from the small area data by speckle imaging processing. Details of the processing will be described later.

  In step S605b, the second correction unit generates small area data in which fluctuation has been corrected from the small area data by a second correction process different from the speckle imaging process.

  In step S606, the fluctuation-corrected small area data generated in step S605a / b is combined to generate fluctuation-corrected image data.

  As described above, it is a feature of the present embodiment that fluctuation correction is performed by different processes according to the intensity of atmospheric fluctuation to suppress artifacts and obtain a high-quality output image.

  Next, details of the processing in step S603 will be described. FIG. 7 is a flowchart illustrating a flow of a process performed by the parameter calculation unit 203 according to the present embodiment. As an effect of atmospheric turbulence, a local displacement occurs in the image. In this processing, the displacement is evaluated to calculate the parameter. At this time, it is necessary to evaluate separately from “camera shake” and “subject movement” that cause displacement of the image as in the case of atmospheric turbulence. Therefore, the non-uniformity (variation) of the displacement amount in the plane is evaluated by paying attention to the property that the direction and amount of displacement are non-uniform in the plane, which is characteristic of atmospheric turbulence.

  In step S701, a motion vector is calculated for a plurality of points (n) in the small area data.

  In step S702, the variation of the motion vector (atmospheric fluctuation intensity B, degree of fluctuation influence) is calculated. n motion vectors

Against, atmospheric turbulence intensity _{B 1} is,

Can be calculated as follows. here

Is

Mean vector, and || indicates the absolute value of the vector.

By the above processing, the parameter B ₁ showing the strength of atmospheric turbulence on the basis of the image data can be calculated.

At step S604, the the atmospheric turbulence intensity _{B 1} is, determines that a high phase estimation accuracy in speckle imaging process when a predetermined threshold value _{B th} smaller, the process proceeds to step S605a. Meanwhile atmospheric turbulence intensity B ₁ is, determines that the low phase estimation accuracy in speckle imaging process when the threshold B _th greater, the process proceeds to step S605b.

  Next, details of the processing in step S605a will be described. FIG. 8 is a flowchart illustrating the flow of the speckle imaging process performed by the first correction unit.

  In step S801, the preprocessing unit 501 performs preprocessing on each of the small area data. The pre-processing includes any of defective pixel processing, alignment, luminance inclination correction, and apodization. In the defective pixel processing, a pixel that does not have a normal value due to dust existing in the optical system or a defect of the pixel of the image sensor is detected in advance, and the value of the pixel is determined by an average value of values of peripheral pixels after imaging. This is the replacement process. In the alignment, when a positional shift occurs between frames due to vibration of the imaging apparatus or the like, the positional shift of each frame with respect to a certain reference frame is estimated, and the positional shift of each frame is corrected. Assuming that the displacement is limited to translation, an existing method such as Fourier correlation can be used to estimate the displacement. The luminance gradient correction is a process of fitting a primary plane to the luminance distribution of each small area data and subtracting the primary plane from the luminance value of each pixel. The primary plane may be limited to the inclination 0, in which case the average luminance value of the small area data is subtracted. In the apodization, a window function is applied to the small area data in order to suppress inconsistency that occurs at the boundary when integrating the results of image processing for each small area data. Not all of the above processes need to be executed, and only the necessary processes need to be executed.

  In step S802, the Fourier transform unit 502 performs a Fourier transform on each frame of the small area data to generate a Fourier spectrum.

In step S803, the bi-spectrum calculation unit 503 generates bi-spectrum data corresponding to each of the small area data using the Fourier spectrum obtained in step S802. n th frame of the Fourier spectrum I by the _n spectrum I _B of small area _{data, n} represents is defined by Equation 5.

  Here, u and v are two-dimensional data representing coordinates on the frequency plane, respectively. The Fourier spectrum is two-dimensional data, while the bi-spectrum is four-dimensional data.

  In step S804, the phase estimating unit 504 estimates the phase of the Fourier spectrum O of the output image using the bi-spectrum data. As shown in Patent Document 1, this phase is determined by Expression 6.

  Here, <> represents an average over a plurality of frames. According to Equation 6, if the phases at three points (0, 0), (1, 0), and (0, 1) on the frequency plane are determined in advance as initial phases, the phases at arbitrary coordinates are sequentially determined. Can be determined. As the phases of these three points, for example, the value of the phase of the Fourier spectrum of the small area data obtained by averaging a plurality of frames is used. The initial phase may be determined for four or more points on the frequency plane.

  In step S805, the average power spectrum calculation unit 505 calculates the average of the power spectra of the small area data of a plurality of frames.

  In step S806, the amplitude estimating unit 506 estimates the amplitude of the Fourier spectrum of the output image using the average power spectrum A obtained in step S805. Specifically, for example, as shown in Reference Document 1, it is calculated according to Equation 7.

  Here, T is an optical transfer function of the imaging device, and S is a speckle transfer function. T may be obtained by imaging a point light source in a state where there is no disturbance such as atmospheric turbulence in advance, or may be obtained by numerical calculation using a model. S is a function that expresses the deterioration of an image due to atmospheric turbulence, and may be measured in advance and stored, or may be obtained by numerical calculation using a model. Alternatively, a plurality of measurement data and models different depending on the intensity of the atmospheric turbulence may be stored, and selected and used based on the parameter indicating the intensity of the atmospheric variability calculated in step S603.

  In step S807, the image synthesis unit 507 integrates the phase and amplitude of the Fourier spectrum O of the output image, generates an output image by inverse Fourier transform, and outputs synthesized image data.

  Through the above-described speckle imaging processing, it is possible to perform correction for improving image quality on an image whose image quality has been degraded due to the influence of atmospheric turbulence. According to this speckle imaging processing, while high image quality can be achieved, there is a demerit that when the intensity of atmospheric turbulence is high or sensor noise is strong, a specific artifact occurs and visibility is reduced.

  On the other hand, the effects of atmospheric turbulence can be reduced and a high-quality image can be output even by “blind deconvolution processing” or “averaging processing” that is correction processing by the second correction unit 204. The correction performed by the second correction unit 204 cannot achieve higher image quality than the correction performed by the first correction unit 205. However, even when the intensity of atmospheric turbulence is high or the sensor noise is strong, stable image quality is obtained. Images can be output.

  Hereinafter, the details of the processing in step S606 will be described. In step S606, the image combining unit 207 acquires the first correction result output from the first correction unit 205 and the second correction result output from the second correction unit 206. The image combining unit 207 outputs final image data by combining the correction results received in small area units.

  As described above, according to the present embodiment, two effects of image quality improvement and artifact reduction can be appropriately obtained by the determination of the phase estimation accuracy determination unit 204.

  In the above-described embodiment, only one of the first correction unit 205 and the second correction unit performs the correction process based on the result of the phase estimation accuracy determination unit 204. On the other hand, as shown in FIG. 14, it is possible to perform the correction processing by the first correction unit 1405 and the second correction unit 1406 regardless of the result of the phase estimation accuracy determination unit 1404. The determination result of the phase estimation accuracy determination unit 1404 is used to determine which of the first correction result and the second correction result is adopted.

Further, the first correction result and the second correction result can be "combined" based on the determination result of the phase estimation accuracy determination unit. For example, by adding a weighted first correction result and the second correction results atmospheric turbulence intensity B ₁ as the weighting coefficient, to obtain a more appropriate correction result.

  In the present embodiment, an example will be described in which good fluctuation correction is realized by calculating a temporal change of a displacement amount as a parameter indicating the intensity of atmospheric fluctuation from input image data.

  The configuration of the present embodiment is different from the first embodiment in the function of the parameter calculation unit 203 in FIG. In this embodiment, the parameter calculation unit 203 in FIG. 2 has functions as a motion vector calculation unit 401 and a time change calculation unit 402, as shown in FIG.

  The processing of the present embodiment is different from the processing of the first embodiment in the processing content of step S603.

  The details of the process in step S603 will be described below. FIG. 9 is a flowchart illustrating a flow of a process performed by the parameter calculation unit 203 in step S603 of the present embodiment. As an effect of atmospheric turbulence, a local displacement occurs in the image. In this processing, the displacement is evaluated to calculate the parameter. At this time, it is necessary to evaluate separately from “camera shake” and “subject movement” that cause displacement of the image as in the case of atmospheric turbulence. Therefore, attention is paid to the characteristic that the time change of the direction and amount of displacement is large, which is peculiar to atmospheric turbulence, and the time change of the amount of displacement in the plane is evaluated.

  In step S901, a motion vector is calculated for a plurality of frames in the small area data.

  In step S902, a time change of the motion vector is calculated. motion vector for k frames

Respect, time variation _{B 2,} for example,

Can be calculated as follows. Here, <> represents an average over a plurality of frames. || indicates the absolute value of the vector. The temporal change of the motion vector may be calculated at a plurality of points in the small area data, and the statistic (such as an average value or a median value) may be calculated.

  Through the above processing, a parameter indicating the intensity of atmospheric turbulence can be calculated based on the image data.

  In the first and second embodiments, an example will be described in which input image data is divided into a plurality of small area data, and a fluctuation correction method is changed based on a parameter indicating the intensity of atmospheric fluctuation calculated for each of the small area data. did. In the present embodiment, an example will be described in which atmospheric turbulence correction is performed on the entire input image data by the same processing. The result of the atmospheric turbulence correction used at that time is selectively output based on a parameter indicating the degree of influence of the atmospheric turbulence calculated for the entire input image data.

  As illustrated in FIG. 10, the information processing apparatus 100 according to the present exemplary embodiment includes an image data acquisition unit 1001, a parameter calculation unit 1003, a phase estimation accuracy determination unit 1004, an image division unit 1002, a first correction unit 1005, and a second It has a function as a correction unit 1006 and an image combination unit 1007. Each unit in FIG. 10 has the same function as the block with the same name in FIG.

  FIG. 11 is a flowchart illustrating the flow of the process according to the present embodiment.

  In step S1101, the image data obtaining unit 1001 obtains image data including a plurality of frames to be processed.

  In step S1102, the parameter calculation unit 1003 calculates a parameter indicating the intensity of atmospheric turbulence at the time of acquiring the image data. In the processing described with reference to FIG. 7 or FIG. 9, a parameter indicating the intensity of fluctuation is calculated for the small area data obtained by dividing the image data. However, here, the same processing is performed on the entire input image data. As another method, a user may input a parameter indicating the intensity of atmospheric turbulence to the operation unit 110 and use the parameter. As another method, it may be calculated from information on weather conditions (temperature, wind speed, etc.) at the time of acquiring the image data.

  In step S1103, the phase estimation accuracy determination unit 1004 determines the phase estimation accuracy in the speckle imaging processing. When the parameter calculated in step S1102 is smaller than the threshold calculated in advance, it is determined that the phase estimation accuracy is high, and the process proceeds to step S1104. If the parameter calculated in step S1102 is larger than the threshold calculated in advance, it is determined that the phase estimation accuracy is low, and the process proceeds to step S1107.

  In step S1104, the image dividing unit 1002 divides the image data into a plurality of small area data. Here, duplication of the plurality of small area data is allowed. The size of the small area data is preferably smaller than a range (isoplanatic patch) in which the influence of atmospheric turbulence can be considered to be constant. The next step S1105 is performed for each of the plurality of small area data.

  In step S1105, the first correction unit 1005 generates small area data whose fluctuation has been corrected from the small area data by speckle imaging processing.

  In step S1106, the fluctuation-corrected small area data generated in step S1105 is combined to generate fluctuation-corrected image data.

  In step S1107, the second correction unit generates image data subjected to fluctuation correction from the image data by a second correction process different from the speckle imaging process.

  As described above, the correction result by the first correction unit and the correction result by the second correction unit are selectively output according to the degree of influence of atmospheric fluctuation. As a result, it is possible to obtain high-quality output image data while suppressing artifacts.

  In the present embodiment, an example will be described in which a parameter indicating the intensity of noise included in an input image is calculated to realize good fluctuation correction.

  In the speckle imaging process, when the noise intensity is high, the phase estimation accuracy is reduced, and as a result, a unique artifact is generated in the reconstructed image and visibility is reduced.

  The configuration of the present embodiment is different from the third embodiment in the function of the parameter calculation unit 1003 in FIG. In the present embodiment, the parameter calculation unit 1003 in FIG. 10 has functions as a shooting information acquisition unit 1201 and a noise intensity calculation unit 1202 as shown in FIG.

  The processing according to the present embodiment is different from the processing according to the third embodiment in the processing content of step S1102.

  Hereinafter, details of the processing in step S1102 will be described. FIG. 13 is a flowchart illustrating a flow of a process performed by the parameter calculation unit 1003 in the present embodiment.

  In step S1301, the shooting information obtaining unit 1201 obtains shooting information at the time of obtaining image data to be processed. The photographing information includes information that affects noise intensity such as ISO sensitivity.

  In step S1302, the noise intensity calculator 1202 calculates the noise intensity. As a method of calculating the noise intensity, a method of referring to an LUT (look-up table) and a method of applying a model formula are exemplified.

  As described above, it is possible to perform fluctuation correction by different processes according to the noise intensity, suppress artifacts, and obtain a high-quality output image.

(Other embodiments)
The present invention is also realized by executing the following processing. That is, software (program) for realizing the functions of the above-described embodiments is supplied to a system or apparatus via a network or various storage media, and a computer (or CPU, MPU, or the like) of the system or apparatus reads the program and reads the program. This is the process to be performed.

100 information processing device 101 CPU
102 RAM
103 ROM
109 display device 201 image data acquisition unit 202 image division unit 203 parameter calculation unit 204 phase estimation accuracy determination unit 205 first correction unit 206 second correction unit 207 image combining unit

Claims (13)

  The first correction result of the plurality of image data by the first correction process and the first correction process are based on the degree of influence of the fluctuation of the plurality of image data whose image quality has been deteriorated due to the influence of the atmospheric fluctuation. An image processing apparatus having an output unit that outputs a second correction result of the plurality of image data obtained by different second correction processes.   The image processing apparatus according to claim 1, further comprising a combining unit configured to generate combined image data based on the first correction result and the second correction result.   The image processing apparatus according to claim 1, wherein the output unit selectively outputs the first correction result and the second correction result based on the degree of influence.   The image processing apparatus according to claim 1, wherein the output unit combines and outputs the first correction result and the second correction result based on the degree of influence.   The image processing apparatus according to claim 1, wherein the output unit does not perform the first processing or the second processing based on the degree of influence. The plurality of images are composed of a plurality of blocks,
The image processing apparatus according to claim 1, wherein the output unit outputs the first correction result and the second correction result in units of the plurality of blocks.   The image processing apparatus according to claim 1, wherein the degree of influence is determined based on a variation of a motion vector among the plurality of pieces of image data.   The image processing apparatus according to claim 1, wherein the degree of influence is determined based on a temporal change of a motion vector between the plurality of pieces of image data.   The image processing apparatus according to claim 1, wherein the degree of influence is determined based on an input from a user.   The image processing apparatus according to claim 1, wherein the degree of influence includes information indicating noise of the plurality of pieces of image data.   The image processing apparatus according to claim 1, wherein the degree of influence is determined based on weather conditions when the plurality of images are acquired. A program for causing a computer to function as the image processing device according to claim 1.   The first correction result of the plurality of image data by the first correction process and the first correction process are based on the degree of influence of the fluctuation of the plurality of image data whose image quality has been deteriorated due to the influence of the atmospheric fluctuation. An image processing apparatus having an output unit that outputs a second correction result of the plurality of image data obtained by different second correction processes.

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