JP5824423B2 - Illumination light color estimation device, illumination light color estimation method, and illumination light color estimation program - Google Patents

Description

  The present invention relates to an illumination light color estimation device, an illumination light color estimation method, and an illumination light color estimation program for estimating illumination light color from an image whose illumination light color is unknown.

The pixel values (R, G, B) in the color image are obtained from both illumination light (E (λ): spectral distribution) and object color (ρ (λ): surface reflectance), as shown in the following equation. Integration with spectral sensitivity characteristics (￣r (λ), ￣g (λ), ￣b (λ)) (￣ is on r, g, and b, respectively) of the imaging system such as a camera (Expression (1)). Note that the index i given to the lower right of R, G, B, and ρ indicates the pixel value and the surface reflectance of the i-th object in the image, respectively.

  From the equation (1), even if the spectral sensitivity characteristic of the imaging system is known, the illumination light color cannot be estimated under the condition that the color of the object is unknown. Typical examples of conventional illumination light color estimation techniques include a technique that uses properties relating to illumination light and the color of an object, and a technique that uses a range in which a predetermined color changes under any illumination light. The former includes, for example, a technique for obtaining the color of a highlight area as an illumination light color by utilizing the property that a highlight area composed of a specular reflection component of a subject is a color reflecting the illumination light color (for example, non-patent literature) 1) or a method of obtaining an average pixel value of an image as an illumination light color based on a hypothesis that a subject is averaged in gray (gray hypothesis) (for example, Non-Patent Document 2 and Non-Patent Document 3). See).

  In the latter technique, there are a method using white as a predetermined color (for example, see Patent Document 1) and a method using a color with high saturation (for example, see Non-Patent Document 4). The method using white uses the fact that the achromatic subject area reflects the illumination light color, and obtains a color close to the white color (pixel value) under various illumination light colors from the input image. In the method using high-saturation color, the range that the color of the subject in the world can take under various illuminations is obtained in advance, and the color of the illumination light is determined from the degree of coincidence with the color distribution of the input image. Is what you want.

Japanese Patent Laid-Open No. 2002-290988

G.J.Klinker, S.A.Shafer, T.Kanade, "Using a color reflection model to separate highlights from object color", Proceedings of the First International Conference on Computer Vision, pp.145-150 (1987). R. Gershon and A.D. Jepson, "The Computation of Color Constant Descriptors in Chromatic Images", Color Research and Application, Vol. 14, No. 6, pp. 325-334 (1989). Kawamura, Yonemura, Otani, Matsuura, “Proposal of Illumination Light Color Estimation Method Based on Gray Hypothesis”, 2011 Image Electronics Society 39th Annual Conference, R2-3, (2011). G.D.Finlayson, S.D.Hordley and I.Tastl, "Gamut constrained illuminant estimation", International Journal of Computer Vision, vol.67, no.1, pp.93-109 (2006).

  However, the conventional method described above has the following problems. In the case of a method that uses properties related to illumination light or the color of an object, it cannot be applied to an environment where the properties used as assumptions do not hold. That is, in the method of Non-Patent Document 1, it is essential that the image has a specular reflection component. In the method of Non-Patent Document 2, the color of the subject in the image satisfies the gray hypothesis, that is, the color of the subject in the scene. It is a condition that the average becomes gray, and when these conditions are not satisfied, there is a problem that the estimation accuracy deteriorates.

  Further, the technique of Non-Patent Document 3 represents the problem in Non-Patent Document 2 by expressing a representative color by clustering the color distribution in the image into the same color region and selecting a color that satisfies the gray hypothesis from the image. However, if the colors included in the image are greatly biased or the number of colors is extremely small, it is impossible to extract a color that satisfies the gray hypothesis as described above. There is a problem that gets worse.

  On the other hand, the technique described in Patent Document 1 is based on the premise that an achromatic subject exists in the image, and there is a problem that the estimation accuracy deteriorates if this assumption is not satisfied. Further, in the method of Non-Patent Document 4, since the high saturation color is less affected by the illumination light color, the overlap range of the color areas under different illumination lights is large, and when the input image has few high saturation colors, Since most of the pixel values of the input image are included in the overlap range, there is a problem in that the illumination light color cannot be estimated accurately.

  The present invention has been made in view of such circumstances, and the illumination light color estimation apparatus and illumination light color capable of estimating the illumination light color without depending on the color bias and the number of colors included in the image. An object is to provide an estimation method and an illumination light color estimation program.

The present invention provides an illumination color estimating device for estimating the illumination light color of the input color image, storing information defining low saturation color range on the chromaticity diagram of the under each of the plurality of illumination light A low saturation color region database , a high saturation color region database storing information defining a range on a chromaticity diagram of a high saturation color under each of a plurality of illumination lights, and the low saturation color region database; Illumination light color candidate extraction means for extracting illumination light color candidates for the input color image with reference to the input information, and obtaining color information of illumination light colors corresponding to the illumination light color candidates from the high saturation color region database. Color data generation means for generating color data obtained by adding the acquired color information to color information of the input color image; and cluster generation means for generating a cluster in which similar colors are grouped with respect to the obtained color data And before Select the first color data from the cluster, in the selection of the second and subsequent, in said cluster in a position diagonally across the cluster and the blackbody locus close to the average of all colors of Selected A color data selection means for selecting the next color data from the image , an average calculation means for obtaining an average of the selected color data, and the color data selected using the selected color data and the average result of the color data is a gray hypothesis A gray hypothesis determination unit that determines whether or not the gray hypothesis is satisfied, and an illumination light color estimation unit that estimates an average color of the color data determined to satisfy the gray hypothesis as an illumination light color, To do.

  The present invention is characterized by further comprising color information conversion means for converting the input color image into a color image independent of an input / output device before the illumination light color estimation process.

  The present invention further includes illumination light color conversion means for converting the input color image into an image under a desired illumination light color based on the estimated illumination light color of the input color image.

The present invention is, input color image in order to estimate the illumination light color, low chroma color area database storing information defining low saturation color range on the chromaticity diagram of the under each of the plurality of illumination light And an illumination light color estimation method performed by an illumination light color estimation apparatus including a high saturation color region database storing information defining a range on a chromaticity diagram of a high saturation color under each of a plurality of illumination lights, Illuminating light color candidate extraction step for extracting illumination light color candidates of the input color image with reference to information stored in the low saturation color region database, and illumination light corresponding to the illumination light color candidates A color data generation step of acquiring color information of a color from a high-saturation color region database and generating the color data by adding the acquired color information to the color information of the input color image; and similar to the obtained color data The color of A cluster generation step of generating a cluster, select the first color data among the cluster, in the second and subsequent selected across the cluster and the blackbody locus close to the average of all colors of Selected A color data selection step for selecting next color data from the clusters at diagonal positions, an average calculation step for obtaining an average of the selected color data, an average of the selected color data and the color data A gray hypothesis determination step for determining whether the color data selected using the result satisfies a gray hypothesis, and an illumination for estimating an average color of the color data determined to satisfy the gray hypothesis as an illumination light color And a light color estimation step.

  The present invention further includes an illumination light color conversion step of converting the input color image into an image under a desired illumination light color based on the estimated illumination light color of the input color image.

  The present invention is an illumination light color estimation program for causing a computer to function as the illumination light color estimation apparatus.

  According to the present invention, it is possible to estimate the illumination light color without depending on the color deviation or the number of colors included in the image.

It is a block diagram which shows the structure of one Embodiment of this invention. It is a block diagram which shows the structure of the illumination light color calculation part 107 shown in FIG. It is explanatory drawing which shows the data contained in the low chroma color area DB shown in FIG. It is explanatory drawing which shows the data contained in the low chroma color area DB shown in FIG. It is explanatory drawing which shows the data contained in the high saturation color area | region DB shown in FIG. It is a flowchart which shows operation | movement of the apparatus shown in FIG. It is explanatory drawing which shows the operation | movement which acquires an illumination light color candidate. It is explanatory drawing which shows the operation | movement which produces | generates a cluster.

  Hereinafter, an illumination light color estimation apparatus according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of the embodiment. The illumination light color estimation apparatus shown in FIG. 1 is configured by a computer device, and includes an image input unit 101, an image / data storage unit 102, a color information conversion unit 103, a low saturation color region database (DB) 104, and illumination light color candidates. An extraction unit 105, a high saturation color region database (DB) 106, an illumination light color calculation unit 107, an illumination light color conversion unit 108, and an image output unit 109 are provided. FIG. 2 is a block diagram showing a detailed configuration of the illumination light color calculation unit 107 shown in FIG. The illumination light color calculation unit 107 includes a color data generation unit 201, a cluster generation unit 202, a color selection unit 203, an average calculation unit 204, and a gray hypothesis determination unit 205.

  The image input unit 101 inputs a target scene as a digital color image using an input device such as a digital camera or a scanner. The image / data storage unit 102 stores the color image input by the image input unit 101, the image obtained by each process, and data of calculation results during the process, and corresponds to the memory of the computer device. Note that the image stored in the image / data storage unit 102 is composed of three components of R (red), G (green), and B (blue), but in a color space such as HSV or YCbCr. It is also possible to use an image having a pixel value as a coordinate.

  The color information conversion unit 103 performs data conversion between an image space and a physical space. For example, a color space that does not depend on device characteristics from an image pixel value represented by RGB, for example, a CIE (International Lighting Commission). ) Conversion into XYZ tristimulus values defined in (1) and vice versa. Note that the color space that does not depend on the device characteristics may be a CIELAB space or a CIELV space defined by CIE.

  The low-saturation color region DB 104 stores color information of a range that can be taken by low-saturation colors under various illumination lights, and is configured from a set of chromaticity coordinates, for example, as shown in FIG. FIG. 3 is an explanatory diagram showing data included in the low saturation color region DB shown in FIG. In FIG. 3, text data representing the range of possible low saturation colors under a certain illumination light is represented by chromaticity coordinates, and one line corresponds to one type of color, that is, chromaticity coordinates. FIG. 4 is a plot of an xy chromaticity diagram. FIG. 4 is an explanatory diagram showing data included in the low saturation color region DB shown in FIG. In FIG. 4, a region surrounded by a plurality of small triangular broken lines along the black body radiation locus (solid curve) corresponds to a possible range of low chroma colors under each illumination light. The black body radiation locus is obtained by converting a spectral distribution emitted by an ideal object (black body) expressed by Planck's radiation equation according to color temperature into color information and projecting it onto the color space. As shown by the solid line, it becomes one curve. It is known that the spectral characteristics of natural light (sunlight) are close to black body radiation, and the color of natural light can be approximated by the color on the black body radiation locus. Since the artificial light is designed to be close to the appearance under natural light, the color of the artificial light can be regarded as being in the vicinity of the black body radiation locus.

The illumination light color candidate extraction unit 105 extracts illumination light color candidates from the color information of the input color image that does not depend on the device characteristics obtained by the color information conversion unit 103 with reference to the low saturation color region DB 104. In the following description, the xy chromaticity calculated from the XYZ tristimulus values using the formula (2) is used as the color information, but the color information is not limited to this. An input color image having a large number of colors included in low-saturation color regions that differ only in the illumination light color condition is selected, and illumination light colors corresponding to the corresponding low-saturation color region are extracted as candidates. For extraction of illumination light color candidates, the top N (N ≧ 1) are selected in descending order of the number of colors included in the low saturation color region, and illumination light color data corresponding to the extracted low saturation color region is selected. Is stored in the image / data storage unit 102.

  The high-saturation color region DB 106 stores a range in which high-saturation color under various illumination lights can be taken, and is configured in a data format as shown in FIG. 3 like the low-saturation color region DB 104. FIG. 5 shows an example of a high saturation color region (a large triangular region indicated by a broken line) for each illumination light. FIG. 5 is an explanatory diagram showing data included in the high saturation color region DB shown in FIG.

  The illumination light color calculation unit 107 obtains the illumination light color using the high saturation color region corresponding to the illumination light color obtained by the illumination light color candidate extraction unit 105. The color data generation unit 201 virtually generates a color within a high saturation color region corresponding to one of the illumination light color candidates and stores the color in the image / data storage unit 102. The color generated here is added to the color information of the input color image converted into the color space independent of the input device by the color information conversion unit 103 stored in the image / data storage unit 102.

  The cluster generation unit 202 gives a similar color on the chromaticity diagram to the color information obtained by adding the result of the color data generation unit 201 to the color information of the input color image stored in the image / data storage unit 102. Combine and classify into clusters of similar hues. The color selection unit 203 selects colors in order from each cluster, and the average calculation unit 204 obtains the average of the selected colors. Finally, the gray hypothesis determination unit 205 determines whether or not a plurality of colors selected by the color selection unit 203 satisfy the gray hypothesis from the result of the average calculation unit 204. Information on the illumination light color is stored in the image / data storage unit 102. Here, the illumination light color information is represented by one point or vector in the color space, such as coordinates in the RGB color space or XYZ tristimulus values.

  The illumination light color conversion unit 108 applies the illumination light color to the input color image obtained from the image input unit 101 stored in the image / data storage unit 102 based on the result obtained by the illumination light color calculation unit 107. Is converted into a color under the desired illumination light (for example, white) and stored in the image / data storage unit 102. The image output unit 109 outputs the illumination light color-converted image stored in the image / data storage unit 102 using an output device. Here, the output device is, for example, a monitor, a printer, a projector, or the like.

Next, the processing operation of the illumination light color estimation apparatus shown in FIG. 1 will be described with reference to FIG. FIG. 6 is a flowchart showing the processing operation of the illumination light color estimation apparatus shown in FIG. First, the image input unit 101 inputs a color image and stores the input color image in the image / data storage unit 102 (step S1). Subsequently, the color information conversion unit 103 converts the input color image into an image from which the characteristics of the input device are removed (step S2). The characteristics of the input device correspond to the spectral sensitivity characteristics shown in Expression (1), but in many cases, they are not directly available. In this case, the γ coefficient is based on the relational expressions shown in Expression (3) and Expression (4). And a 3 × 3 matrix (α _{i, j} ) can be used instead. Thereby, XYZ tristimulus values are obtained from the RGB pixel values and converted to color information.

  After conversion to color information independent of the characteristics of the input device, the illumination light color candidate extraction unit 105 extracts illumination light color candidates using data stored in the low saturation color region DB 104 (step S3). Here, an operation of extracting illumination light color candidates will be described with reference to FIG. FIG. 7 shows color information obtained by converting a color range (solid triangle) and a pixel value of an input color image into a color space independent of the input device based on the color information stored in the low saturation color region DB 104. (Small ◯ in the figure) is superimposed and displayed on the same chromaticity diagram. The number of colors of the input color image included in each color range is counted, and the top N (N ≧ 1) are extracted from those having the largest number of colors, and the corresponding illumination light color is extracted as a candidate.

  Next, the illumination light color calculation unit 107 reads color information corresponding to the illumination light color obtained in step S3 from the high saturation color region DB 106, and calculates the illumination light color (step S4). The illumination light color is calculated as follows. The high-saturation color area DB 106 stores chromaticity information representing the outline of the color area in the data format shown in FIG. 3 for each illumination light color, and creates a color included in the area (step S41). The color generation method is, for example, a method in which a grid point of 0.01 increments in the horizontal and vertical directions in the high saturation color region on the chromaticity diagram (the region surrounded by the broken line in FIG. 5) is a virtual color, There is a method of generating with random numbers, but any method may be used.

  Next, the color information obtained in step S41 is added to the color information obtained in step S2, and the cluster generation unit 202 classifies the colors having similar hues and generates clusters (step S42). FIG. 8 shows an operation for generating a cluster from color information. FIG. 8 is an explanatory diagram showing an operation of generating a cluster. In FIG. 8, the color information is indicated by “·”, and the black body radiation locus is indicated by a broken line. The cluster generation unit 202 integrates colors in the vicinity on the chromaticity coordinates, and generates the upper and lower colors as separate clusters across the black body radiation locus (broken line). FIG. 8 shows an example in which four clusters A to D are generated. Further, FIG. 8 shows a state where clusters are generated on the xy chromaticity diagram, but an HS plane (a plane composed of hue and saturation) in the HSV space representing three attributes of color, or u ′ in a uniform color space. It may be on the v ′ chromaticity diagram.

  Next, the color selection unit 203 selects one color as an initial value (step S43). The color to be selected first may be determined by a random number, or may be a color with the highest luminance or a color with high saturation. After that, the color selection unit 203 selects the next color from the cluster located diagonally across the first selected color and the black body radiation locus (step S44). To select the next color from the cluster at the diagonal position, for example, when the first color is selected from the cluster indicated by symbol A in FIG. 8, the next color is selected from the cluster indicated by symbol D. Means. If the first color is a cluster of code C, the next color is selected from the cluster of code B.

  Since the black-body radiation locus corresponds to the color range of the illumination light, that is, the range where the achromatic color can be taken, the color information has a property of being distributed concentrically around the color of the illumination light. Such color selection is performed by utilizing the fact that the colors at the diagonal positions with respect to the black body radiation locus are in the relationship of opposite colors and the average of them may be an achromatic color. The average calculation unit 204 calculates the average color for the color selected by the color selection unit 203 (step S45). Here, the average color may be an average of XYZ tristimulus values corresponding to the selected color, and the result may be converted into chromaticity coordinates, or directly with respect to the xy chromaticity of the selected color. You may calculate the average.

Next, the gray hypothesis determination unit 205 determines whether or not the selected plurality of colors satisfy the gray hypothesis (step S46). The gray hypothesis determination unit 205 determines that the gray hypothesis is satisfied when both of the conditions shown in equations (5) and (6) are satisfied, and obtains the average of the selected colors as the illumination light color.

The determination formula of Formula (5) is to determine whether the average (P _k ) of the selected colors is in the vicinity of the black body radiation locus L, and dis (L, P _k ) is the black body radiation locus. Represents the distance between L and the average _Pk of the selected color. th ₁ is a threshold value. The judgment formula of Formula (6) indicates that the variation in average color is small, and the difference σ _k −σ _k−1 of the standard deviation of the average value of up to K colors is smaller than the threshold th _2. On condition that If at least one of the two conditions is not satisfied, it is determined that the gray hypothesis is not established for the set of colors selected at this time, and the next color selection (step S44) is performed again. ) And continue processing. In the second and subsequent color selections, a cluster close to the average color of all the colors selected up to the previous one is obtained instead of the previous one, and the black body radiation locus is inserted. To select the next color from the cluster at the diagonal position.

This processing operation (steps S41 to S46) is performed for all illumination light color candidates obtained in the illumination light color candidate extraction step S3 (step S47). An illumination light color having the smallest error between the illumination light color in the illumination light color candidate extraction step S3 and the illumination light color obtained by the processes in steps S41 to S46 is determined as the illumination light color (step S48). Then, the illumination light color conversion unit 108 removes the influence of the illumination light color estimated based on Expression (7) from the input color image and generates an image converted into a color under a predetermined illumination light (step). S5).

x, y is the estimated illumination light chromaticity, x ', y' is the chromaticity of the illumination light prearranged, for example, a white light source or daylight, of the illumination light used in the standard, such as illuminant D ₆₅ Let it be chromaticity. X, Y, and Z are color information that does not depend on the characteristics of the input device obtained from the input color image in the color information conversion unit 103, and X ′, Y ′, and Z ′ are the results after the illumination light conversion.

  Finally, the color information conversion unit 103 obtains a conversion matrix and a γ coefficient according to the characteristics of the output device based on the equations (3) and (4), and applies the result to the result obtained in step S5 to obtain a color image (Step S6) and output by the image output unit 109 (step S7).

  As described above, by estimating the illumination light color from the color image whose illumination light color is unknown, it is possible to convert the color of the image under a predetermined illumination (for example, a standard white light source). In particular, the illumination light color can be estimated without depending on the color bias and the number of colors included in the image, so that it is possible to easily generate an image with the illumination light color changed using the result. It becomes.

  The program for realizing the functions of the processing units in FIGS. 1 and 2 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system and executed to execute illumination. Light color estimation processing may be performed. Here, the “computer system” includes an OS and hardware such as peripheral devices. The “computer system” includes a WWW system having a homepage providing environment (or display environment). The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

  The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

  As mentioned above, although embodiment of this invention has been described with reference to drawings, the said embodiment is only the illustration of this invention, and it is clear that this invention is not limited to the said embodiment. is there. Accordingly, additions, omissions, substitutions, and other changes of the components may be made without departing from the technical idea and scope of the present invention.

  The present invention can be applied to applications in which it is essential to estimate the illumination light color from an image whose illumination light color is unknown.

  DESCRIPTION OF SYMBOLS 101 ... Image input part, 102 ... Image / data storage part, 103 ... Color information conversion part, 104 ... Low-saturation color area DB (database), 105 ... Illumination light color candidate extraction 106, high saturation color region DB (database), 107 ... illumination light color calculation unit, 108 ... illumination light color conversion unit, 109 ... image output unit, 201 ... color data generation unit 202 ... Cluster generation unit 203 ... Color selection unit 204 ... Average calculation unit 205 ... Gray hypothesis determination unit

Claims (6)

An illumination light color estimation device for estimating the illumination light color of an input color image,
A low chroma color area database storing information defining a range on the chromaticity diagram of low chroma color under each of the plurality of illumination light,
A high-saturation color region database storing information defining a range on a chromaticity diagram of a high-saturation color under each of a plurality of illumination lights;
Illumination light color candidate extracting means for extracting illumination light color candidates of the input color image with reference to information stored in the low saturation color region database;
Color data generating means for acquiring color information of an illumination light color corresponding to the illumination light color candidate from a high saturation color region database, and generating color data in which the acquired color information is added to color information of the input color image; ,
Cluster generating means for generating clusters in which similar colors are grouped with respect to the obtained color data;
The first color data is selected from the clusters, and in the second and subsequent selections, the clusters close to the average of all the selected colors and the clusters located diagonally across the blackbody radiation locus are selected. Color data selection means for selecting the next color data from the inside ,
An average calculating means for calculating an average of the selected color data;
Gray hypothesis determination means for determining whether the selected color data and the color data selected using the average result of the color data satisfy a gray hypothesis;
An illumination light color estimation device comprising: an illumination light color estimation unit that estimates an average color of the color data determined to satisfy the gray hypothesis as an illumination light color.   The illumination light color according to claim 1, further comprising color information conversion means for converting the input color image into a color image independent of an input / output device before the illumination light color estimation process. Estimating device.   3. The illumination light color conversion means for converting the input color image into an image under a desired illumination light color based on the estimated illumination light color of the input color image. Illumination light color estimation device. To estimate the illumination light color of the input color image, and the low saturation color region database storing information defining low saturation color range on the chromaticity diagram of the under each of a plurality of illumination light, a plurality of An illumination light color estimation method performed by an illumination light color estimation device including a high saturation color region database storing information defining a range on a chromaticity diagram of a high saturation color under each illumination light,
An illumination light color candidate extraction step of extracting illumination light color candidates of the input color image with reference to information stored in the low saturation color region database;
A color data generation step of acquiring color information of the illumination light color corresponding to the illumination light color candidate from a high saturation color region database, and generating color data in which the acquired color information is added to the color information of the input color image; ,
A cluster generation step of generating a cluster in which similar colors are grouped with respect to the obtained color data;
The first color data is selected from the clusters, and in the second and subsequent selections, the clusters close to the average of all the selected colors and the clusters located diagonally across the blackbody radiation locus are selected. A color data selection step for selecting the next color data from the inside ,
An average calculating step for calculating an average of the selected color data;
A gray hypothesis determination step of determining whether the selected color data and the color data selected using the average result of the color data satisfy a gray hypothesis;
An illumination light color estimation method comprising: an illumination light color estimation step that estimates an average color of the color data determined to satisfy the gray hypothesis as an illumination light color.   5. The illumination light according to claim 4, further comprising an illumination light color conversion step of converting the input color image into an image under a desired illumination light color based on the estimated illumination light color of the input color image. Color estimation method.   An illumination light color estimation program for causing a computer to function as the illumination light color estimation apparatus according to any one of claims 1 to 3.

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