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

Abstract

To analyze a captured image at a high speed, thereby reducing the time required for mapping a result of the analysis.SOLUTION: A reference image region extraction unit 101 extracts, from a captured image 150, a partial image region in which luminance values of pixels are evaluated to be uniform, as a reference image region 151. A micro image region dividing unit 102 divides the captured image 150 into a plurality of micro image regions 152 significantly smaller in size than the reference image region 151. A statistics calculation unit 103 calculates, for each of the micro image regions 152, statistics 153 of luminance values of pixels in the regions obtained by adding each of the micro image regions 152 to the reference image region 151. A statistics mapping unit 104 maps the statistics 153 calculated for each of the micro image regions 152, in association with positions of the micro image regions 152 in the captured image 150.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a technique for analyzing a captured image and mapping the analysis results. [Background technology]

[0002] As a technique related to the present disclosure, CFAR (Constant False Alarm Rate). CFAR is a threshold algorithm that reduces the effects of local noise and clutter to extract targets with a certain false alarm rate. CFAR can be used to analyze captured images and map the analysis results. CFAR is used, for example, to detect images of ships from images of the sea surface captured from above. When CFAR is used to detect ships, the ships are separated from the sea surface in the mapping image. When using CFAR to detect ships, for example CA-CFAR (Cell Averaging-CFAR), the average value of the area around the ship (reference) is calculated and the brightness is normalized by that average value. By doing this, the brightness of the sea surface becomes an approximately constant value independent of wave height or swell. Areas brighter than the threshold are then separated from the sea surface as ships. As a technique to which CFAR is applied, for example, there is a technique disclosed in Non-Patent Document 1. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] M. Sekine and Y. Mao, Weibull Radar Clutter, Peter Peregrinus, London, 1990 Summary of the Invention [Problem to be solved by the invention]

[0004] CFAR requires the user to set the values ​​of many parameters, as follows: 1) Size of the target cell 2) Number of guard cells and reference cells 3) Size of guard cell and reference cell In addition, with CFAR, the optimal value of each parameter changes depending on the local distribution of brightness in the background image and the size of the ship to be detected, so trial and error is required for each captured image to set the optimal value for each parameter. As described above, when analyzing captured images using CFAR, it is difficult to set the optimal value for each parameter. Therefore, it takes time to set the optimal value for each parameter, and as a result, it takes a long time to map the analysis results.

[0005] One of the main objectives of the present disclosure is to solve the above-mentioned problems. More specifically, the main objective of the present disclosure is to perform high-speed analysis of captured images and shorten the time it takes for the analysis results to be mapped. [Means for solving the problem]

[0006] The image processing device according to the present disclosure includes: a reference image region extraction unit that extracts, from the captured image, a partial image region in which the luminance values ​​of pixels within the region are evaluated to be uniform, as a reference image region; a minute image region dividing unit that divides the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation unit that calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that is the sum of the reference image region and each minute image region; The image capturing device further includes a statistical value mapping section that maps the statistical values ​​calculated for each minute image region in correspondence with the position of each minute image region within the captured image. [Effects of the Invention]

[0007] According to the present disclosure, it is possible to analyze captured images at high speed and reduce the time it takes for the analysis results to be mapped. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of the functional configuration of an image processing device according to a first embodiment. [Figure 2] FIG. 1 is a diagram showing an example of the hardware configuration of an image processing device according to a first embodiment. [Figure 3] 4 is a flowchart showing an example of the operation of the image processing device according to the first embodiment. [Figure 4] FIG. 2 is a diagram showing an example of a reference image region according to the first embodiment. [Figure 5] 3A and 3B are diagrams showing examples of a scanning frame and a minute image region according to the first embodiment. [Figure 6] FIG. 3 is a conceptual diagram illustrating an example of calculating a statistical value according to the first embodiment. [Figure 7] FIG. 2 is a diagram showing an example of a captured image according to the first embodiment. [Figure 8] FIG. 2 is a diagram showing an example of a reference image region according to the first embodiment. [Figure 9] FIG. 3 is a diagram showing an example of a statistical value map according to the first embodiment. [Figure 10] FIG. 10 is a diagram showing an example of the functional configuration of an image processing device according to a second embodiment. [Figure 11] 4 is a flowchart showing an example of the operation of the image processing device according to the first embodiment. [Figure 12] FIG. 10 is a diagram showing an example of a reference image region according to the second embodiment. [Figure 13] FIG. 10 is a conceptual diagram illustrating an example of calculating a statistical value according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments will be described with reference to the drawings. In the following description of the embodiments and in the drawings, the same reference numerals denote the same or corresponding parts.

[0010] Embodiment 1 ***Configuration Description*** FIG. 1 shows an example of the functional configuration of an image processing device 100 according to this embodiment. FIG. 2 shows an example of the hardware configuration of the image processing device 100 according to this embodiment. The image processing device 100 may be mounted on a flying object such as an artificial satellite or an airplane, or may be located in a ground station that manages the flying object.

[0011] The image processing device 100 according to this embodiment is a computer. The operation procedure of the image processing device 100 corresponds to an image processing method, and the program that realizes the operation of the image processing device 100 corresponds to an image processing program.

[0012] As shown in FIG. 2, the image processing device 100 includes, as hardware, a processor 911, a main storage device 912, an auxiliary storage device 913, and a communication device 914. 1, the image processing device 100 includes, as functional components, a reference image region extraction unit 101, a small image region division unit 102, a statistical value calculation unit 103, a statistical value mapping unit 104, and a display unit 105. The functions of these functional components are realized, for example, by a program. The auxiliary storage device 913 stores programs that realize the functions of these functional components. These programs are loaded from the auxiliary storage device 913 into the main storage device 912. Then, the processor 911 executes these programs to perform the operations of these functional components. FIG. 2 shows a schematic diagram of a state in which the processor 911 is executing a program that implements the functions of the functional components.

[0013] The image processing device 100 acquires a captured image 150 captured by a flying object such as an artificial satellite or an airplane. The image processing device 100 analyzes the captured image 150 and outputs a statistical value map 160 in which the analysis results are mapped. The image processing device 100 may be mounted on a flying object such as an artificial satellite or an airplane, or may be located at a ground station.

[0014] The captured image 150 is, for example, an image of the sea surface captured from above by an aircraft, etc. The captured image 150 is, for example, a synthetic aperture radar image (hereinafter referred to as a SAR image). The captured image 150 may also be an image of an object other than the sea surface. The captured image 150 may be, for example, an image of any of a water surface other than the sea surface, outer space, a desert, a forest, and ground covered with a uniform material (asphalt, soil, etc.).

[0015] These captured images 150 include partial image regions in which the brightness values ​​of the pixels within the region are evaluated to be uniform. When the captured image 150 is an image of the sea surface, the pixel brightness values ​​are uniform in the partial image area showing only the sea surface. That is, for example, in the case of a color image captured by an optical camera, the entire partial image area showing only the sea surface is almost uniformly blue. Furthermore, if the captured image 150 is an image of outer space, the pixel brightness values ​​are uniform in the partial image area showing only outer space. That is, for example, in the case of a color image captured by an optical camera, the entire partial image area showing only outer space is almost uniformly black.

[0016] On the other hand, the captured image 150 has elements whose pixel luminance values ​​are different from the pixel luminance values ​​of most other regions. If the captured image 150 is an image of the sea surface, it contains elements other than the sea surface, such as ships, offshore plants, islands, etc. The brightness values ​​of the pixels in the areas of these elements are different from the brightness values ​​of the pixels on the sea surface. Furthermore, if the captured image 150 is an image of outer space, it contains extra elements other than outer space, such as celestial bodies and artificial satellites, etc. The brightness values ​​of the pixels in the areas of these extra elements are different from the brightness values ​​of the pixels in outer space.

[0017] For the sake of simplicity, the following description will be given assuming that the captured image 150 is an SAR image of the sea surface. However, the following description also applies to cases where the captured image 150 is an image of a water surface other than the sea surface, outer space, a desert, a forest, or ground covered with the same material (asphalt, soil, etc.).

[0018] Next, an example of the functional configuration of the image processing device 100 will be described with reference to FIG. In FIG. 1, the image processing device 100 is independent from the imaging device, but the image processing device 100 and the imaging device may be integrated. In the following description, an example in which the image processing device 100 is independent from the imaging device will be described.

[0019] The reference image region extraction unit 101 acquires a captured image 150 . Then, the reference image region extraction unit 101 extracts a reference image region 151, as shown in Fig. 4, from the captured image 150. The reference image region 151 is a partial image region that is evaluated as having uniform pixel brightness values ​​within the region. Fig. 4 will be described in detail later. For example, as described above, the reference image area extraction unit 101 extracts a partial image area from the captured image 150 that is evaluated as showing only the sea surface as the reference image area 151. The reference image region extraction unit 101 outputs the captured image 150 to the small image region division unit 102. The reference image region extraction unit 101 also outputs the reference image region 151 to the statistical value calculation unit 103. The processing performed by the reference image region extraction unit 101 corresponds to a reference image region extraction process.

[0020] The minute image region dividing section 102 divides the captured image 150 into a plurality of minute image regions 152, each having a size significantly smaller than the size of the reference image region 151. Fig. 5 shows an example of the minute image region 152. The details of Fig. 5 will be described later. The minute image region dividing section 102 divides the captured image 150 into a plurality of minute image regions 152 each having a size of, for example, 5 percent or less of the size of the reference image region 151 . The minute image region dividing unit 102 outputs a plurality of minute image regions 152 to the statistical value calculation unit 103 . The processing performed by the minute image region dividing unit 102 corresponds to minute image region dividing processing.

[0021] The statistical value calculation unit 103 calculates, for each of the plurality of minute image regions 152, a statistical value 153 of the luminance values ​​of pixels in an area obtained by adding each minute image region 152 to the reference image region 151. The statistical value calculation unit 103 calculates one or more of the standard deviation, variance, skewness, kurtosis, and moment as the statistical value 153 . Then, the statistical value calculation unit 103 outputs the calculated statistical value 153 to the statistical value mapping unit 104 . The processing performed by the statistical value calculation unit 103 corresponds to statistical value calculation processing.

[0022] The statistical value mapping unit 104 maps the statistical value 153 calculated for each minute image region 152 so that it corresponds to the position of each minute image region 152 in the captured image 150. Then, the statistical value mapping unit 104 generates a statistical value map 160 that indicates the mapping result. The statistical value mapping unit 104 outputs the generated statistical value map 160 to the display unit 105 . The processing performed by the statistical value calculation unit 103 corresponds to a statistical value mapping process.

[0023] The display unit 105 displays the statistical value map 160 generated by the statistical value mapping unit 104 . If the image processing device 100 is mounted on an air vehicle, the display unit 105 transmits the statistical value map 160 to the ground station in a format that can be displayed on a display at the ground station. Furthermore, when the image processing device 100 is installed in a ground station, the display unit 105 displays the statistical value map 160 on a display screen of the ground station.

[0024] ***Explanation of Operation*** FIG. 3 shows an example of the operation of the image processing device 100 according to this embodiment. An example of the operation of the image processing device 100 according to this embodiment will be described below with reference to FIG.

[0025] In step S101 , the reference image region extraction unit 101 acquires the captured image 150 , and further extracts the reference image region 151 from the captured image 150 . The reference image region extraction unit 101 outputs the captured image 150 to the small image region division unit 102. The reference image region extraction unit 101 also outputs the reference image region 151 to the statistical value calculation unit 103.

[0026] FIG. 4 shows an example of extraction of the reference image region 151 by the reference image region extraction unit 101. In FIG. For example, the reference image area extraction unit 101 extracts a partial image area of ​​about 100 pixels square as the reference image area 151 .

[0027] The reference image region extraction unit 101 may extract the reference image region 151 in accordance with a specification by a user of the image processing device 100. Alternatively, the reference image region extraction unit 101 may extract the reference image region 151 by analyzing the captured image 150. In the former case, the user of the image processing device 100 visually checks the captured image 150 and manually specifies a partial image area in which the brightness values ​​of the pixels within the area are evaluated to be uniform. Then, the reference image area extraction unit 101 extracts the partial image area specified by the user as the reference image area 151. In the latter case, the reference image region extraction unit 101 divides the captured image 150 into multiple partial image regions. Each partial image region has the same size as the reference image region 151. When dividing the image, auxiliary information such as digital elevation data or land cover classification data may be used to select regions where the distribution of brightness values ​​is estimated to be uniform, such as regions with a constant elevation value or regions with widely uniform land cover classification values. Furthermore, the reference image region extraction unit 101 calculates statistical values ​​of the brightness values ​​of the pixels within each partial image region. The reference image region extraction unit 101 then extracts, as the reference image region 151, partial image regions whose statistical values ​​are not outliers from among the multiple partial image regions. Outliers are determined using a common statistical test. Examples of statistical tests include a boxplot test, a Smirnoff-Grubbs test, and a Thompson test. A partial image region that is not an outlier is, for example, a region among the multiple partial image regions whose statistical value is closest to the median. The reference image region extraction unit 101 calculates one or more of the following statistical values: standard deviation, variance, skewness, kurtosis, and moment.

[0028] Next, in step S102, the minute image region dividing unit 102 divides the captured image 150 into minute image regions 152. FIG. 5 is a conceptual diagram illustrating an example of division into minute image regions 152 by the minute image region dividing unit 102. In FIG.

[0029] The minute image region dividing unit 102 sets a scanning frame 1520 that is significantly smaller than the size of the reference image region 151 . The minute image region dividing unit 102 sets a scanning frame 1520 having a size of, for example, 5 percent or less of the size of the reference image region 151. If the reference image region 151 is a partial image region of about 100 pixels square, for example, the minute image region dividing unit 102 sets a scanning frame 1520 of several to 10 and a half pixels square. Then, the minute image region dividing unit 102 scans the captured image 150 with the scanning frame 1520, and divides the captured image 150 into a plurality of minute image regions 152. For example, the minute image region dividing unit 102 moves the scanning frame 1520 by one pixel at a time to scan the captured image 150, and obtains a plurality of minute image regions 152 that are shifted from each other by one pixel. 5 shows the thus obtained minute image regions 152-1, 152-2, and 152-Z. The minute image region 152-1 is the minute image region located at the leftmost position on the top row of the captured image 150. The minute image region 152-2 is the minute image region located immediately to the right of the minute image region 152-1. The minute image region 152-Z is the minute image region located at the rightmost position on the bottom row of the captured image 150. If there is no need to distinguish between the minute image region 152-1, the minute image region 152-2, and the minute image region 152-Z, they will be collectively referred to as the minute image region 152.

[0030] 5, the minute image region dividing unit 102 also scans the range of the reference image region 151 using the scanning frame 1520, and divides the range of the reference image region 151 into minute image regions 152. Alternatively, the minute image region dividing unit 102 may not divide the range of the reference image region 151 into minute image regions 152.

[0031] Next, in step S103, the statistical value calculation unit 103 calculates, for each minute image region 152, a statistical value 153 of the luminance values ​​of pixels in the region obtained by adding the minute image region 152 to the reference image region 151. FIG. 6 is a conceptual diagram illustrating an example of calculation of the statistical value 153 by the statistical value calculation unit 103. In FIG.

[0032] The statistical value calculation unit 103 adds the minute image region 152-1 to the reference image region 151, and calculates a statistical value 153 of the luminance values ​​of the pixels in the regions of the reference image region 151 and the minute image region 152-1. Furthermore, the statistical value calculation unit 103 adds the minute image region 152-2 to the reference image region 151, and calculates a statistical value 153 of the luminance values ​​of the pixels in the regions of the reference image region 151 and the minute image region 152-2. The statistical value calculation unit 103 performs the same operation up to the minute image region 152-Z. In this way, the statistical value calculation unit 103 calculates, for each minute image region 152, a statistical value 153 of the luminance values ​​of the pixels in the region obtained by adding the minute image region 152 to the reference image region 151. Then, the statistical value calculation unit 103 outputs the calculated statistical value 153 to the statistical value mapping unit 104 .

[0033] Next, in step S104, the statistical value mapping unit 104 maps the statistical values ​​153 calculated for each minute image region 152 in correspondence with the position of each minute image region 152 in the captured image 150, thereby generating a statistical value map 160. That is, the statistical value mapping unit 104 maps the statistical value 153 for the minute image region 152-1 to the leftmost position in the top row of the captured image 150. The statistical value mapping unit 104 also maps the statistical value 153 for the minute image region 152-2 to the immediate right of the statistical value 153 for the minute image region 152-1. Finally, the statistical value mapping unit 104 maps the statistical value 153 for the minute image region 152-Z to the rightmost position in the bottom row of the captured image 150. The statistical value mapping unit 104 outputs the statistical value map 160 to the display unit 105 .

[0034] Finally, in step S105, the display unit 105 outputs the statistical value map 160.

[0035] For example, it is assumed that the reference image region extraction unit 101 acquires a captured image 150 shown in FIG. In FIG. 7, the white area is the captured image area of ​​the sea surface. Furthermore, the captured image 150 shown in FIG. 7 includes high luminance ranges 154 and 155, which have higher luminance values ​​than other ranges (sea surface). High brightness area 154 and high brightness area 155 are considered to be images of redundant elements such as ships.

[0036] As shown in FIG. 8, the reference image region extraction unit 101 extracts, as a reference image region 151, a range 156 (range of the sea surface) in which the luminance value is evaluated to be uniform in the captured image 150 of FIG.

[0037] FIG. 9 shows an example of a statistical map 160 generated from the captured image 150 of FIG. In the statistical value map 160, areas where the statistical value 153 is less than the threshold are represented in white, and areas where the statistical value 153 is equal to or greater than the threshold are represented in black. In the statistical value map 160 in Fig. 9, the ranges indicated by reference numerals 157 and 158 correspond to the high brightness range 154 and the high brightness range 155 in Fig. 7. The other ranges in Fig. 9 correspond to the sea surface range in Fig. 7. In this way, the sea surface and redundant elements can be separated with high accuracy in the statistical value map 160. Therefore, instead of CFAR, the method using the statistical value 153 according to this embodiment can be applied to the analysis of the captured image 150.

[0038] As mentioned above, CFAR requires the setting of many parameter values, such as: 1) Size of the target cell 2) Number of guard cells and reference cells 3) Size of guard cell and reference cell In addition, with CFAR, the optimal value of each parameter changes depending on the local distribution of brightness in the background image and the size of the ship to be detected, so trial and error is required for each captured image to set the optimal value for each parameter.

[0039] In contrast, in the method according to the present embodiment, the only parameters that need to be set are as follows: 1) Size of the reference image area 151 2) Size of the micro image area 152 The size of the reference image region 151 and the size of the minute image region 152 can be easily set by the user of the image processing device 100, and can be fixedly applied to all captured images 150. In other words, in this embodiment, there is no need to go through trial and error for each captured image 150 in order to set the optimal value. 9, a threshold value for the statistical value 153 (a threshold value for distinguishing between white and black displays) is also required, but this threshold value can also be determined theoretically. Therefore, trial and error for each captured image 150 is not required.

[0040] Here, the method of calculating the statistical value 153 by the statistical value calculation unit 103 will be described in detail. The statistical value calculation unit 103 can calculate the statistical value 153 at high speed by the following method. In the following, a method for the statistical value calculation unit 103 to calculate the skewness and kurtosis as the statistical value 153 will be described. The symbols used in the explanation are as follows: x: Reference image area + small image area. The number of data points is N. x =N y +N z y: Reference image region. The number of data points is N y z: A small image area. The number of data points is N. z

[0041] Skewness s of x x and kurtosis k x is expressed by equations (1) and (2).

[0042]

number

[0043] The statistical value calculation unit 103 calculates in advance the sum of first powers (cumulative total) (sum(1)), sum of second powers (sum(2)), sum of third powers (sum(3)), and sum of fourth powers (sum(4)) for the reference image region y.

[0044]

number

[0045] Furthermore, the statistical value calculation unit 103 calculates the following for each minute image region.

[0046] First, the statistical value calculation unit 103 calculates the average μ of x as follows: x Ask for.

[0047]

number

[0048] The statistical value calculation unit 103 calculates the denominator of the skewness (Equation (1)) and the kurtosis (Equation (2)), that is, the variance of x, as shown in Equation (4).

[0049]

number

[0050] The statistical value calculation unit 103 calculates the first term on the right side of the formula (4) by multiplying the sum of first powers (cumulative total) (sum (1)) and the sum of squares (sum (2)) calculated in advance, and the average μ x Using (Equation (3)), the calculation is performed as follows:

[0051]

number

[0052] Regarding the second term (sum (5)) on the right side of the formula (4), the statistical value calculation unit 103 calculates the sum of the data z i and the mean μ of x x Calculate the sum as described in (5) using and. The statistical value calculation unit 103 raises or squares the equation (4) to find the denominators of the equations (1) and (2).

[0053]

number

[0054] The statistical value calculation unit 103 calculates the numerator of the skewness (Equation (1)) as shown in Equation (6).

[0055]

number

[0056] The statistical value calculation unit 103 calculates the first term on the right side of the equation (6) by calculating the sum of first powers (cumulative total) (sum(1)), sum of second powers (sum(2)), and sum of third powers (sum(3)) that have been calculated in advance, and the average μ x Using (Equation (3)), the calculation is performed as follows:

[0057]

number

[0058] The statistical value calculation unit 103 calculates the second term (sum (6)) on the right side of the equation (6) as the data z i and the mean μ of x x Calculate the sum as described in (6) using

[0059]

number

[0060] Similarly, the statistical value calculation unit 103 calculates the numerator of the kurtosis (Equation (2)) as shown in Equation (8).

[0061]

number

[0062] The statistical value calculation unit 103 calculates the first term on the right side of the formula (8) by multiplying the sum of first powers (cumulative total) (sum(1)), sum of second powers (sum(2)), sum of third powers (sum(3)), and sum of fourth powers (sum(4)) calculated in advance, and the average μ of x. x Using (Equation (3)), the calculation is performed as follows:

[0063]

number

[0064] The statistical value calculation unit 103 calculates the second term (sum (7)) on the right side of the equation (8) using the data z i and the mean μ of x x(Equation (3)) and calculate the sum as described in (7).

[0065]

number

[0066] ***Explanation of the effect of the embodiment*** In this embodiment, captured images can be analyzed with fewer parameters than with CFAR. Furthermore, the values ​​of each parameter can be easily set by the user, eliminating the need for trial and error for each captured image. Therefore, according to this embodiment, the captured image can be analyzed at high speed, and the time required for the analysis results to be mapped can be reduced.

[0067] Embodiment 2, In this embodiment, differences from the first embodiment will be mainly described. The matters not explained below are the same as those in the first embodiment.

[0068] FIG. 10 shows an example of the functional configuration of an image processing device 100 according to this embodiment. In FIG. 10, compared to FIG. 1, a distribution attribute estimation unit 106 and a random number reference image region generation unit 107 are added. In addition, in this embodiment, the operations of the reference image region extraction unit 101 and the statistical value calculation unit 103 are different from those in the first embodiment.

[0069] In this embodiment, the reference image region extraction unit 101 extracts a partial image region as the reference image region 151. Furthermore, the reference image region extraction unit 101 outputs the reference image region 151 to the distribution attribute estimation unit 106, rather than to the statistical value calculation unit 103. Note that instead of a single partial image region, multiple partial image regions of the same size may be extracted as multiple reference image regions 151. Furthermore, the reference image region extraction unit 101 may output multiple reference image regions 151 to the distribution attribute estimation unit 106, rather than to the statistical value calculation unit 103.

[0070] The distribution attribute estimation unit 106 acquires one or more reference image regions 151 from the reference image region extraction unit 101 . Then, for each reference image region 151, the distribution attribute estimation unit 106 estimates the attribute of the distribution of the luminance values ​​of the pixels in the reference image region 151. More specifically, the distribution attribute estimation unit 106 estimates the shape of the distribution and the values ​​of parameters that define the shape of the distribution as the attributes of the distribution. When the distribution is known, the distribution attribute estimation unit 106 estimates parameters that define the shape of the distribution. For example, in the case of a normal distribution, the unit estimates the mean and standard deviation, and in the case of a gamma distribution, the unit estimates the values ​​of parameters θ and k included in the following equation (10). When the distribution is unknown, the distribution attribute estimation unit 106 assumes several distribution shapes, for example, assuming a gamma distribution, exponential distribution, normal distribution, etc. for the distribution of brightness values, and estimates parameters that define the shape of each distribution. From a probability distribution constructed using each assumed distribution and each parameter, data is generated using random numbers in an amount equal to the number of pixels contained in the reference image area 151, and a histogram of that data is then created. The histogram artificially created using random numbers is compared with the histogram of the reference image area 151, and the most suitable distribution and parameters are selected. When a plurality of partial image regions 151 are selected, the distribution attribute estimation unit 106 selects one of the distribution attributes estimated for the plurality of reference image regions 151 . Then, the distribution attribute estimation unit 106 outputs a selected distribution attribute 159 , which is an attribute of the selected distribution, to the random number reference image area generation unit 107 . The processing performed by the distribution attribute estimation unit 106 corresponds to distribution attribute estimation processing.

[0071]

number

[0072] The random number reference image area generation unit 107 generates, for each reference image area 151, a plurality of random numbers according to the selected distribution attribute 159, which is the attribute of the distribution selected by the distribution attribute estimation unit . Furthermore, the random number reference image area generation unit 107 uses the generated random number values ​​as brightness values ​​of pixels within the area to generate a partial image area of ​​the same size as the reference image area 151. The partial image area generated by the random number reference image area generation unit 107 is called a random number reference image area 170. The random number reference image area generating unit 107 outputs the generated random number reference image area 170 to the statistical value calculating unit 103 . The processing performed by the random number reference image area generating unit 107 corresponds to random number reference image area generation processing.

[0073] The statistical value calculation unit 103 acquires the random number reference image area 170 from the random number reference image area generation unit 107 . Then, for each of the multiple minute image regions 152, the statistical value calculation unit 103 calculates a statistical value 153 of the luminance values ​​of the pixels in the region obtained by adding each minute image region 152 to the random number reference image region 170. As in the first embodiment, statistical value calculation section 103 outputs statistical value 153 to statistical value mapping section 104 .

[0074] The other components shown in FIG. 10 operate in the same manner as in the first embodiment.

[0075] The functions of the distribution attribute estimation unit 106 and the random number reference image area generation unit 107 are also realized by a program, similar to the reference image area extraction unit 101 etc. The program realizing the functions of the distribution attribute estimation unit 106 and the random number reference image area generation unit 107 is executed by the processor 901.

[0076] ***Explanation of Operation*** FIG. 11 shows an example of the operation of the image processing device 100 according to this embodiment. An example of the operation of the image processing device 100 according to this embodiment will be described below with reference to FIG.

[0077] In step S201, the reference image region extraction unit 101 acquires the captured image 150, and further extracts one or more reference image regions 151 from the captured image 150. The reference image region extraction unit 101 outputs the captured image 150 to the minute image region division unit 102, and outputs each reference image region 151 to the distribution attribute estimation unit .

[0078] FIG. 12 is a conceptual diagram showing an example of the operation of the reference image region extraction unit 101 in this embodiment. In this embodiment, when the captured image 150 is an image of the sea surface, the reference image region extraction unit 101 extracts a range 156 that is evaluated as showing only the sea surface (a range that is evaluated as having a uniform brightness value). In this case, as shown in Fig. 12, a plurality of ranges 156 that are evaluated as showing only the sea surface (a range that is evaluated as having a uniform brightness value) may be extracted and used as a plurality of reference image regions 151. In FIG. 12, the reference image region extraction unit 101 extracts the range 156-1 as the reference image region 151-1, the range 156-2 as the reference image region 151-2, and the range 156-3 as the reference image region 151-3. In the following description, when there is no need to distinguish between the reference image area 151-1, the reference image area 151-2, and the reference image area 151-3, they will be collectively referred to as the reference image area 151.

[0079] Step S102 is the same as in the first embodiment, and therefore the explanation will be omitted.

[0080] In parallel with step S102, in step S202, the distribution attribute estimation unit 106 estimates, for each reference image region 151, the attributes of the distribution of luminance values ​​of pixels in the reference image region 151. More specifically, for each reference image region 151, the distribution attribute estimation unit 106 estimates the shape of the distribution and the values ​​of parameters that define the shape of each distribution. For example, the distribution attribute estimation unit 106 generates a plurality of distribution shape candidates (such as gamma distribution, exponential distribution, and normal distribution) that are candidates for the shape of the distribution for each reference image region 151. Furthermore, the distribution attribute estimation unit 106 estimates, for each distribution shape candidate, the values ​​of parameters that define the distribution shape candidate as parameter value candidates. Then, for each reference image region 151, the distribution attribute estimation unit 106 selects, from the plurality of pairs of estimated distribution shape candidates and parameter value candidates, the pair of distribution shape candidate and parameter value candidate that best matches the histogram of the luminance values ​​of pixels in the reference image region 151 as the distribution attribute. Furthermore, the distribution attribute estimation unit 106 selects the most appropriate distribution attribute from among the distribution attributes estimated for the multiple reference image regions 151. Specifically, among the distribution attributes estimated from the multiple reference image regions 151, an attribute that is not an outlier is selected, such as the mean and standard deviation in the case of a normal distribution. The outlier determination is performed using a general statistical test. Examples of statistical tests include a test using a boxplot, a Smirnoff-Grubbs test, and a Thompson test. An attribute that is not an outlier is, for example, an attribute among the multiple attributes whose statistics are closest to the median. The distribution attribute estimation unit 106 may estimate the distribution attribute using the gamfit function of MATLAB (registered trademark) or the like. The distribution attribute estimation unit 106 outputs the attribute of the selected distribution to the random number reference image area generation unit 107 as a selected distribution attribute 159 .

[0081] Next, in step S203, the random number reference image area generating unit 107 generates a plurality of random numbers according to the selected distribution attribute 159. The random number reference image area generating unit 107 generates random numbers using, for example, the gamrnd function of MATLAB (registered trademark). If the reference image area 151 is a partial image area of ​​approximately 100 pixels square and the distribution of brightness values ​​in the reference image area 151 follows a gamma distribution, the random number reference image area generation unit 107 generates random numbers, for example, 101 x 101, that follow a gamma distribution. Furthermore, the random number reference image area generating unit 107 generates a random number reference image area 170 using the generated random number values. The random number reference image area generating unit 107 outputs the generated random number reference image area 170 to the statistical value calculating unit 103 .

[0082] In step S204, the statistical value calculation unit 103 calculates, for each of the multiple minute image regions 152, a statistical value 153 of the luminance values ​​of pixels in the region obtained by adding each minute image region 152 to the random number reference image region 170. That is, in this embodiment, the statistical value calculation unit 103 uses the random number reference image area 170 instead of the reference image area 151 to calculate the statistical value 153 for each of the multiple minute image areas 152 . FIG. 13 is a conceptual diagram illustrating an example of calculation of the statistical value 153 by the statistical value calculation unit 103 in step S204. In this embodiment, the calculation method of the statistical value 153 is the same as in the first embodiment.

[0083] Steps S104 and S105 are the same as those in the first embodiment, and therefore the explanation will be omitted.

[0084] In the method according to this embodiment, as in the first embodiment, the statistical value map 160 shown in FIG. 9 can be obtained from the captured image 150 shown in FIG.

[0085] ***Explanation of the effect of the embodiment*** According to this embodiment, a captured image can be analyzed with fewer parameters than CFAR, as in embodiment 1. Furthermore, the values ​​of each parameter can be easily set by the user, eliminating the need for trial and error for each captured image. Therefore, according to this embodiment as well, it is possible to analyze the captured image at high speed and reduce the time until the analysis results are mapped.

[0086] Furthermore, in this embodiment, multiple reference image regions are extracted and distribution attributes are estimated for each reference image region. Furthermore, in this embodiment, the most appropriate distribution attribute is selected from the distribution attributes estimated for the multiple reference image regions. Therefore, outliers can be excluded and reliable distribution shape parameters can be used. Therefore, this embodiment has improved robustness compared to the first embodiment.

[0087] Furthermore, if the distribution of brightness values ​​in the reference image region is a gamma distribution, the parameters θ and k may have incidence angle dependency (especially θ). For example, reference image regions with a small incidence angle, a medium incidence angle, and a large incidence angle are extracted from the captured image, and the parameters θ and k are estimated for each reference image region. If the parameters θ and k are defined as functions of the incidence angle, for example, functions of a first order or lower order of the incidence angle, it is also possible to determine the coefficients of the functions using the least squares method or the like using the estimated multiple parameters θ and k and the incidence angle of each reference image region.

[0088] Although the first and second embodiments have been described above, these two embodiments may be combined and implemented. Alternatively, one of these two embodiments may be partially implemented. Alternatively, these two embodiments may be partially combined and implemented. Furthermore, the configurations and procedures described in these two embodiments may be modified as necessary.

[0089] ***Additional hardware configuration information*** Here, a supplementary explanation of the hardware configuration of the image processing device 100 will be given. The processor 901 shown in FIG. 2 is an integrated circuit (IC) that performs processing. The processor 901 is a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or the like. The main storage device 902 shown in FIG. 2 is a RAM (Random Access Memory). The auxiliary storage device 903 shown in FIG. 2 is a ROM (Read Only Memory), a flash memory, an HDD (Hard Disk Drive), or the like. The communication device 904 shown in FIG. 2 is an electronic circuit that performs data communication processing. The communication device 904 is, for example, a communication chip or a NIC (Network Interface Card).

[0090] The auxiliary storage device 903 also stores an OS (Operating System). At least a part of the OS is executed by the processor 901 . The processor 901 executes at least a part of the OS, and also executes programs that implement the functions of the functional components shown in FIGS. The processor 901 executes the OS, which performs task management, memory management, file management, communication control, and the like. In addition, at least one of information, data, signal values, and variable values ​​indicating the results of processing of the functional components shown in Figures 1 and 10 is stored in at least one of the main memory device 902, the auxiliary memory device 903, and the registers and cache memory within the processor 901. 1 and 10 may be stored on a portable recording medium such as a magnetic disk, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, a DVD, etc. The portable recording medium on which the program for realizing the functions of the functional components shown in Fig. 1 and 10 is stored may be distributed.

[0091] Furthermore, the "part" of at least one of the functional components shown in FIGS. 1 and 10 may be read as a "circuit" or a "step" or a "procedure" or a "process" or a "circuitry." The image processing device 100 may also be realized by a processing circuit, such as a logic integrated circuit (IC), a gate array (GA), an application specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). In this case, the functional components shown in FIGS. 1 and 10 are each implemented as part of a processing circuit. In this specification, the term "processing circuitry" refers to a generic concept that encompasses a processor and a processing circuit. That is, a processor and a processing circuit are each specific examples of "processing circuitry."

[0092] Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) a reference image region extraction unit that extracts, from the captured image, a partial image region in which the luminance values ​​of pixels within the region are evaluated to be uniform, as a reference image region; a minute image region dividing unit that divides the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation unit that calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that is the sum of the reference image region and each minute image region; an image processing device having a statistical value mapping section that maps the statistical value calculated for each minute image region so that it corresponds to the position of each minute image region within the captured image; (Appendix 2) a reference image region extraction unit that extracts, from the captured image, a partial image region in which the luminance values ​​of pixels within the region are evaluated to be uniform, as a reference image region; a distribution attribute estimation unit that estimates an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation unit that generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation unit, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated plurality of random number values ​​as brightness values ​​of pixels within the area; a minute image region dividing unit that divides the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation unit that calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that is the random number reference image region plus each minute image region; an image processing device having a statistical value mapping section that maps the statistical value calculated for each minute image region so that it corresponds to the position of each minute image region within the captured image; (Appendix 3) The distribution attribute estimation unit 3. The image processing device according to claim 2, wherein the shape of the distribution and the value of a parameter defining the shape of the distribution are estimated as attributes of the distribution. (Appendix 4) The distribution attribute estimation unit generating a plurality of distribution shape candidates that are candidates for the shape of the distribution, and estimating, for each distribution shape candidate, the value of a parameter that defines the distribution shape candidate as a parameter value candidate; An image processing device as described in Appendix 3, which selects, from among multiple pairs of distribution shape candidates and parameter value candidates, a pair of distribution shape candidates and parameter value candidates that best matches a histogram of brightness values ​​of pixels in the reference image area as an attribute of the distribution. (Appendix 5) The reference image region extraction unit extracting a plurality of partial image regions of the same size as a plurality of reference image regions, each of which is evaluated as having uniform pixel brightness values ​​within the region; The distribution attribute estimation unit For the plurality of reference image regions, an attribute of a distribution of luminance values ​​of pixels in each of the plurality of reference image regions is estimated, and one of the plurality of distribution attributes estimated for the plurality of reference image regions is selected; The random number reference image area generation unit 3. The image processing device according to claim 2, wherein the distribution attribute estimation unit generates a plurality of random values ​​according to the distribution attribute selected by the distribution attribute estimation unit. (Appendix 6) The minute image region dividing unit 3. The image processing device according to claim 1, wherein the captured image is divided into a plurality of minute image regions each having a size of 5 percent or less of the size of the reference image region. (Appendix 7) The minute image region dividing unit setting a scanning frame having a size significantly smaller than the size of the reference image area; 3. The image processing device according to claim 1, wherein the captured image is scanned with the scanning frame and divided into the plurality of minute image regions. (Appendix 8) The reference image region extraction unit 3. An image processing device according to claim 1, wherein the captured image is divided into a plurality of partial image regions, and for each partial image region, a statistical value of the brightness values ​​of pixels within the region is calculated, and a partial image region among the plurality of partial image regions whose statistical value is not an outlier is extracted as the reference image region. (Appendix 9) The reference image region extraction unit 9. The image processing device according to claim 8, wherein the statistical value is calculated to be one or more of standard deviation, variance, skewness, kurtosis, and moment. (Appendix 10) The statistical value calculation unit 3. The image processing device according to claim 1, wherein the statistical value is calculated to be one or more of a standard deviation, a variance, a skewness, a kurtosis, and a moment. (Appendix 11) The reference image region extraction unit 3. The image processing device according to claim 1, wherein the reference image region is extracted from a captured image of any one of a water surface, outer space, a desert, a forest, and ground covered with the same substance. (Appendix 12) The image processing device further comprises: 3. The image processing device according to claim 1, further comprising a display unit that displays a statistical value map representing a mapping result by the statistical value mapping unit. (Appendix 13) a reference image region extraction process in which a computer extracts a partial image region from the captured image, the partial image region being evaluated as having uniform pixel brightness values ​​within the region, as a reference image region; a minute image region division process in which the computer divides the captured image into a plurality of minute image regions each having a size significantly smaller than the size of the reference image region; a statistical value calculation process in which the computer calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area in which each minute image region is added to the reference image region; and a statistical value mapping process in which the computer maps the statistical values ​​calculated for each minute image region to correspond to the position of each minute image region within the captured image. (Appendix 14) a reference image region extraction process in which a computer extracts a partial image region from the captured image, the partial image region being evaluated as having uniform pixel brightness values ​​within the region, as a reference image region; a distribution attribute estimation process in which the computer estimates an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation process in which the computer generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation process, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated random number values ​​as brightness values ​​of pixels within the area; a minute image region division process in which the computer divides the captured image into a plurality of minute image regions each having a size significantly smaller than the size of the reference image region; a statistical value calculation process in which the computer calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area in which each minute image region is added to the random number reference image region; and a statistical value mapping process in which the computer maps the statistical values ​​calculated for each minute image region to correspond to the position of each minute image region within the captured image. (Appendix 15) a reference image region extraction process for extracting a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a minute image region division process for dividing the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation process for calculating, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that includes the reference image region and each minute image region; and a statistical value mapping process for mapping the calculated statistical values ​​for each minute image region in correspondence with the position of each minute image region within the captured image. (Appendix 16) a reference image region extraction process for extracting a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a distribution attribute estimation process for estimating an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation process that generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation process, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated plurality of random number values ​​as brightness values ​​of pixels within the area; a minute image region division process for dividing the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation process for calculating, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels within an area obtained by adding each minute image region to the random number reference image region; and a statistical value mapping process for mapping the calculated statistical values ​​for each minute image region in correspondence with the position of each minute image region within the captured image. [Explanation of symbols]

[0093] 100 Image processing device, 101 Reference image area extraction unit, 102 Micro image area division unit, 103 Statistical value calculation unit, 104 Statistical value mapping unit, 105 Display unit, 106 Distribution attribute estimation unit, 107 Random number reference image area generation unit, 150 Captured image, 151 Reference image area, 152 Micro image area, 153 Statistical value, 160 Statistical value map, 170 Random number reference image area, 901 Processor, 902 Main memory device, 903 Auxiliary memory device, 904 Communication device.

Claims

1. a reference image region extraction unit that extracts, from the captured image, a partial image region in which the luminance values ​​of pixels within the region are evaluated to be uniform, as a reference image region; a minute image region dividing unit that divides the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation unit that calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that is a sum of the reference image region and each minute image region; an image processing device having a statistical value mapping section that maps the statistical value calculated for each minute image region so that it corresponds to the position of each minute image region within the captured image;

2. a reference image region extraction unit that extracts, from the captured image, a partial image region in which the luminance values ​​of pixels within the region are evaluated to be uniform, as a reference image region; a distribution attribute estimation unit that estimates an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation unit that generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation unit, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated plurality of random number values ​​as brightness values ​​of pixels within the area; a minute image region dividing unit that divides the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation unit that calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that is the random number reference image region plus each minute image region; an image processing device having a statistical value mapping section that maps the statistical value calculated for each minute image region so that it corresponds to the position of each minute image region within the captured image;

3. The distribution attribute estimation unit The image processing apparatus according to claim 2 , wherein the shape of the distribution and the value of a parameter that defines the shape of the distribution are estimated as the attributes of the distribution.

4. The distribution attribute estimation unit generating a plurality of distribution shape candidates that are candidates for the shape of the distribution, and estimating, for each distribution shape candidate, the value of a parameter that defines the distribution shape candidate as a parameter value candidate; 4. The image processing device according to claim 3, wherein, from among a plurality of pairs of distribution shape candidates and parameter value candidates, a pair of distribution shape candidates and parameter value candidates that best matches a histogram of brightness values ​​of pixels in the reference image region is selected as an attribute of the distribution.

5. The reference image region extraction unit extracting a plurality of partial image regions of the same size as a plurality of reference image regions, each of which is evaluated as having uniform pixel brightness values ​​within the region; The distribution attribute estimation unit For the plurality of reference image regions, an attribute of a distribution of luminance values ​​of pixels in each of the plurality of reference image regions is estimated, and one of the plurality of distribution attributes estimated for the plurality of reference image regions is selected; The random number reference image area generation unit The image processing device according to claim 2 , wherein a plurality of random numbers are generated according to the distribution attribute selected by the distribution attribute estimation unit.

6. The minute image region dividing unit 3. The image processing device according to claim 1, wherein the captured image is divided into a plurality of minute image regions each having a size equal to or less than 5 percent of the size of the reference image region.

7. The minute image region dividing unit setting a scanning frame having a size significantly smaller than the size of the reference image area; 3. The image processing device according to claim 1, wherein the captured image is scanned with the scanning frame and the captured image is divided into the plurality of minute image regions.

8. The reference image region extraction unit 3. The image processing device according to claim 1, wherein the captured image is divided into a plurality of partial image regions, and for each partial image region, a statistical value of the brightness values ​​of the pixels within the region is calculated, a statistical test of the statistical value is performed, and a partial image region among the plurality of partial image regions whose statistical value is not an outlier is extracted as the reference image region.

9. The reference image region extraction unit The image processing device according to claim 8 , wherein the statistical values ​​are calculated as one or more of standard deviation, variance, skewness, kurtosis, and moment.

10. The statistical value calculation unit 3. The image processing apparatus according to claim 1, wherein the statistical values ​​are calculated as one or more of standard deviation, variance, skewness, kurtosis, and moment.

11. The reference image region extraction unit 3. The image processing device according to claim 1, wherein the reference image region is extracted from a captured image of any one of a water surface, outer space, a desert, a forest, and ground covered with the same material.

12. The image processing device further comprises:

3. The image processing device according to claim 1, further comprising a display unit that displays a statistical value map that represents the mapping result by the statistical value mapping unit.

13. a reference image region extraction process in which a computer extracts a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a minute image region division process in which the computer divides the captured image into a plurality of minute image regions each having a size significantly smaller than the size of the reference image region; a statistical value calculation process in which the computer calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area in which each minute image region is added to the reference image region; and a statistical value mapping process in which the computer maps the statistical values ​​calculated for each minute image region to correspond to the position of each minute image region within the captured image.

14. a reference image region extraction process in which a computer extracts a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a distribution attribute estimation process in which the computer estimates an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation process in which the computer generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation process, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated plurality of random number values ​​as brightness values ​​of pixels within the area; a minute image region division process in which the computer divides the captured image into a plurality of minute image regions each having a size significantly smaller than the size of the reference image region; a statistical value calculation process in which the computer calculates, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area in which each minute image region is added to the random number reference image region; and a statistical value mapping process in which the computer maps the statistical values ​​calculated for each minute image region to correspond to the position of each minute image region within the captured image.

15. a reference image region extraction process for extracting a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a minute image region division process for dividing the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation process for calculating, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels in an area that includes the reference image region and each minute image region; and a statistical value mapping process for mapping the statistical values ​​calculated for each minute image region in correspondence with the position of each minute image region within the captured image.

16. a reference image region extraction process for extracting a partial image region from the captured image, the pixel brightness values ​​of which are evaluated to be uniform within the region, as a reference image region; a distribution attribute estimation process for estimating an attribute of a distribution of luminance values ​​of pixels in the reference image region; a random number reference image area generation process that generates a plurality of random number values ​​according to the attribute of the distribution estimated by the distribution attribute estimation process, and generates a partial image area of ​​the same size as the reference image area as a random number reference image area by using the generated plurality of random number values ​​as brightness values ​​of pixels within the area; a minute image region division process for dividing the captured image into a plurality of minute image regions having sizes significantly smaller than the size of the reference image region; a statistical value calculation process for calculating, for each of the plurality of minute image regions, a statistical value of the luminance values ​​of pixels within an area obtained by adding each minute image region to the random number reference image region; and a statistical value mapping process for mapping the statistical values ​​calculated for each minute image region in correspondence with the position of each minute image region within the captured image.

Images