Image processing device, storage medium and image processing method

Abstract

An image processing device, a storage medium and an image processing method, wherein the image processing device comprises: an acquisition unit, which acquires a diffuse reflection image of an object surface and a mirror reflection image of the object surface; and a reproduction unit, which uses the diffuse reflection image and the mirror reflection image to reproduce the object surface in a manner such that the higher the chroma of the diffuse reflection image or the value representing the chroma is than that of the mirror reflection image, the stronger the chroma of the illumination is.

Description

Technical Field

[0001] The invention relates to an image processing device, a storage medium and an image processing method. Background Art

[0002] There is a known technique for using a bidirectional reflectance distribution function (hereinafter referred to as BRDF) as a model in order to reproduce the texture (glossiness, unevenness, etc.) of an object surface.

[0003] Patent Document 1 discloses a method of obtaining a BRDF and estimating a model coefficient that fits the obtained BRDF by a least square method or the like as a method of determining a model coefficient of a BRDF.

[0004] Patent document 2 describes a method in which light is irradiated from multiple angles, photographed from multiple angles using a camera to prepare a conversion table between the irradiation angle or photographing angle and the brightness of the photographed image, and the brightness of the focus position is calculated by interpolation processing of the conversion table.

[0005] Patent Document 1: Japanese Patent Application Publication No. 2015-049691

[0006] Patent Document 2: Japanese Patent Application Publication No. 2005-115645 Summary of the invention

[0007] In the technology of obtaining BRDF, the reflection distribution is measured by changing the incident angle or the receiving angle, so it takes time to obtain data. In addition, in the technology of making light incident from multiple angles, photographing from multiple angles with a camera, and using multiple image data to estimate BRDF, it takes a huge amount of time to obtain image data, and in order to obtain accurate gloss information, there are image data obtained by photographing from multiple angles, which requires a large amount of calculation. Therefore, the purpose of the present invention is to provide an image processing device, a storage medium, and an image processing method that can express the texture of the surface of an object with less image data and calculations than the case where the texture of the surface of an object is expressed using multiple image data obtained by photographing from multiple angles with a camera.

[0008] The image processing device described in Scheme 1 includes: an acquisition unit, which acquires a diffuse reflection image of the object surface and a mirror reflection image of the object surface; and a reproduction unit, which uses the diffuse reflection image and the mirror reflection image to reproduce the object surface in a manner such that the higher the chroma of the diffuse reflection image or the value representing the chroma is than that of the mirror reflection image, the stronger the chroma of the illumination is.

[0009] The image processing device described in Scheme 2 includes: an acquisition unit, which acquires a diffuse reflection image of the object surface and a mirror reflection image of the object surface; and an output unit, which uses the diffuse reflection image and the mirror reflection image to output object surface information, wherein the higher the chroma of the mirror reflection image or the value representing the chroma is than that of the diffuse reflection image, the higher the glossiness of the metal on the object surface is represented.

[0010] According to the invention described in claim 2, in the invention described in claim 3, the output unit outputs a difference image between the chroma of the diffuse reflection image and the chroma of the specular reflection image as the object surface information.

[0011] According to the invention described in Scheme 2, in the invention described in Scheme 4, the output unit extracts the maximum chromaticity and the minimum chromaticity in the diffuse reflection image acquired by the acquisition unit, and outputs the differential image between the diffuse reflection image and the mirror reflection image of each of the extracted maximum chromaticity and the minimum chromaticity as the surface information of the object.

[0012] According to the invention described in any one of Schemes 2 to 4, in the invention described in Scheme 5, it includes: an image acquisition unit, which uses the object surface information output by the output unit to select the presence or absence of a color component, and obtains a difference image between the diffuse reflection image and the mirror reflection image corresponding to the presence or absence of the selected color component; a reflection distribution function calculation unit, which uses the diffuse reflection image obtained by the acquisition unit and the difference image obtained by the image acquisition unit to calculate the reflection distribution function of the object surface; and a display unit, which uses the reflection distribution function calculated by the reflection distribution function calculation unit to display the reflection color of the object surface corresponding to the change in the orientation of the object surface.

[0013] According to the invention described in Scheme 5, in the invention described in Scheme 6, when the glossiness of the metal is higher than a preset glossiness of the metal, the image acquisition unit acquires a differential image with a color component, and when the glossiness of the metal is below the preset glossiness of the metal, the image acquisition unit acquires a differential image without a color component.

[0014] According to the invention described in any one of Schemes 2 to 4, in the invention described in Scheme 7, it also includes: an image acquisition unit, which uses the object surface information output by the output unit to select the presence or absence of a color component, and obtains a differential image between the diffuse reflection image and the mirror reflection image corresponding to the presence or absence of the selected color component; and a conversion unit, which uses the diffuse reflection image obtained by the acquisition unit and the differential image obtained by the image acquisition unit to convert into recording material data.

[0015] According to the invention described in Scheme 7, in the invention described in Scheme 8, when the glossiness of the metal is higher than a preset metal glossiness, the image acquisition unit acquires a differential image with a color component, and when the glossiness of the metal is below the preset metal glossiness, the image acquisition unit acquires a differential image without a color component, and when the glossiness of the metal is higher than the preset metal glossiness, the conversion unit converts the diffuse reflection image acquired by the acquisition unit and the differential image with a color component acquired by the image acquisition unit into recording material data containing a metal component, and when the glossiness of the metal is below the preset metal glossiness, the conversion unit converts the diffuse reflection image acquired by the acquisition unit and the differential image without a color component acquired by the image acquisition unit into recording material data not containing a metal component.

[0016] The storage medium according to claim 9 stores an image processing program that causes a computer to function as each unit of the image processing apparatus according to any one of claims 1 to 8.

[0017] The image processing method described in Scheme 10 includes the following steps: an acquisition step, acquiring a diffuse reflection image of the object surface and a mirror reflection image of the object surface; and a reproduction step, using the diffuse reflection image and the mirror reflection image to reproduce the object surface in a manner such that the higher the chromaticity of the diffuse reflection image or the value representing the chromaticity is than that of the mirror reflection image, the stronger the chromaticity of the illumination.

[0018] The image processing method described in Scheme 11 includes the following steps: an acquisition step, acquiring a diffuse reflection image of the object surface and a mirror reflection image of the object surface; and an output step, using the diffuse reflection image and the mirror reflection image to output object surface information, wherein the higher the chroma of the mirror reflection image or the value representing the chroma is than that of the diffuse reflection image, the higher the glossiness of the metal on the object surface is represented.

[0019] Effects of the Invention

[0020] According to the first aspect of the present invention, it is possible to provide an image processing device that can express the texture of an object's surface with less image data and calculations than when expressing the texture of an object's surface using multiple image data obtained by photographing with a camera from multiple angles.

[0021] According to the second aspect of the present invention, it is possible to provide an image processing device that can express the texture of an object's surface with less image data and calculations than when expressing the texture of an object's surface using multiple image data obtained by photographing with a camera from multiple angles.

[0022] According to the third aspect of the present invention, the metallic gloss and the non-metallic gloss on the surface of an object can be distinguished and expressed.

[0023] According to the fourth aspect of the present invention, the metallic gloss and the non-metallic gloss on the surface of an object can be distinguished and expressed.

[0024] According to the fifth aspect of the present invention, it is possible to display an image in which the metallic gloss and the non-metallic gloss on the surface of an object are distinguished and reproduced.

[0025] According to the sixth aspect of the present invention, it is possible to display an image reproduced by distinguishing the gloss of metal from the gloss of non-metal using the difference image having a color component and the difference image having no color component.

[0026] According to the seventh aspect of the present invention, an image in which the metallic gloss and the non-metallic gloss on the surface of an object are distinguished and reproduced can be formed on a recording medium.

[0027] According to the eighth aspect of the present invention, an image reproduced by distinguishing metallic gloss from non-metallic gloss by using a difference image with a color component and a difference image without a color component can be formed on a recording medium.

[0028] According to the 9th scheme of the present invention, it is possible to provide a storage medium that can express the texture of the surface of an object with less image data and calculations than when expressing the texture of the surface of an object using multiple image data obtained by photographing from multiple angles with a camera.

[0029] According to the tenth aspect of the present invention, it is possible to provide an image processing method that can express the texture of an object's surface with less image data and calculations than when expressing the texture of an object's surface using multiple image data obtained by photographing with a camera from multiple angles.

[0030] According to the 11th scheme of the present invention, it is possible to provide an image processing method that can express the texture of the surface of an object with less image data and calculations than using multiple image data obtained by photographing from multiple angles with a camera to express the texture of the surface of an object. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Embodiments of the present invention will be described in detail with reference to the following drawings.

[0032] Figure 1 is a diagram showing a schematic structure of a texture reading device;

[0033] Figure 2 is a block diagram showing the structure of an image processing device according to a first embodiment;

[0034] Figure 3A diagram for explaining an example of determination of the glossiness of a metal by the metallic glossiness determination unit in the first embodiment;

[0035] Figure 4 is a flowchart showing an example of the flow of processing performed by the image processing device involved in the first embodiment;

[0036] Figure 5 is a block diagram showing the structure of an image processing device according to a second embodiment;

[0037] Figure 6 A diagram for explaining an example of determination of the glossiness of a metal by the metallic glossiness determination unit in the second embodiment;

[0038] Figure 7 is a flowchart showing an example of the flow of processing performed by the image processing device involved in the second embodiment;

[0039] Figure 8 is a block diagram showing the structure of a display device according to a third embodiment;

[0040] Fig. 9 is a flowchart showing an example of the flow of processing performed by the display device involved in the third embodiment;

[0041] Fig.10 is a block diagram showing a structure of an image forming apparatus according to a fourth embodiment;

[0042] Fig.11 This is a flowchart showing an example of the flow of processing performed by the image forming apparatus according to the fourth embodiment.

[0043] Explanation of symbols

[0044] 30, 31-image processing device, 32-diffuse reflection image acquisition unit, 34-mirror reflection image acquisition unit, 35-maximum differential chromaticity acquisition unit, 36-RGB→L*C*H* conversion unit, 38-chromaticity differential image acquisition unit, 39-maximum differential chromaticity differential image acquisition unit, 40-metallic gloss degree determination unit, 42-differential image acquisition unit, 44-diffuse reflection distribution function calculation unit, 46-mirror reflection distribution function calculation unit, 52-reflection distribution function calculation unit, 60-concept drawing unit, 62-display unit, 64-data conversion unit, 66-imaging unit. DETAILED DESCRIPTION

[0045] Hereinafter, an example of the present embodiment will be described in detail with reference to the drawings. Figure 1 It is a diagram showing a schematic structure of a texture reading device.

[0046] The texture reading device 10 optically reads the surface characteristics of the object 12 and generates image information representing the reading result. In the present embodiment, the object 12 is described as a plane object, but it is not limited to a plane. The image information generated by the texture reading device 10 includes image information based on diffuse reflection light and image information based on specular reflection light. The texture reading device 10 includes a platen glass 14, a reciprocating table 16, light sources 18, 20, 22, an imaging optical system 24 and a sensor 26. In addition, in the present embodiment, as an example, the example of the texture reading device 10 reading a plane object 12 is described, but a three-dimensional object can also be read.

[0047] In the texture reading device 10 , the components shown in the figure are arranged with a predetermined width in a direction perpendicular to the paper surface. This direction is the main scanning direction of the texture reading device 10 , and the direction indicated by the arrow in the figure is the sub-scanning direction of the texture reading device 10 .

[0048] The platen glass 14 is composed of a transparent glass plate that supports the object 12 to be read. The platen glass 14 is not limited to a glass plate, and may be, for example, an acrylic plate, etc. In addition, although not shown in the figure, a platen cover that covers the platen glass 14 to block external light and clamps the object 12 may be provided.

[0049] The reciprocating stage 16 moves in the sub-scanning direction at a preset speed when reading the object 12. The reciprocating stage 16 is provided with light sources 18, 20, and 22 therein. The light source 18 irradiates light for reading diffusely reflected light from the object 12 by irradiating light from the front side in the moving direction of the reciprocating stage 16 at an incident angle of 45° with respect to the normal direction of the object 12. The light source 20 irradiates light for reading diffusely reflected light from the object 12 by irradiating light from the rear side in the moving direction of the reciprocating stage 16 at an incident angle of 45° with respect to the normal direction of the object 12. On the other hand, the light source 22 irradiates light for reading specularly reflected light from the object 12 by irradiating light at an incident angle of 10° with respect to the normal direction of the object 12.

[0050] The light source 22 is disposed at a position that does not block the main ray of the reflected light. In the embodiment, the incident angle of the light irradiated by the light source 22 is set to 10°, but is not limited to 10°, and may be, for example, about 5° to 10°. Among the reflected light irradiated by the light source 22, the reflected light traveling in the normal direction of the object 12 is read.

[0051] Furthermore, the light source 22 preferably emits light at a narrow angle, for example. When the angle of the light emitted by the light source 22 is large, a cover or the like that limits the angle of the light emitted by the light source 22 may be provided. In addition, the light source 22 is used to read the gloss information of the object 12, and therefore, compared with the light sources 18 and 20, it is preferred that the brightness in the main scanning direction is as uniform and continuous as possible.

[0052] As an example of the light source 22, a white light such as a fluorescent lamp or a rare gas fluorescent lamp (xenon fluorescent lamp, etc.) is suitable. Alternatively, the light source 22 may be a light source in which a plurality of white LEDs are arranged in the main scanning direction and the brightness distribution in the main scanning direction is uniformed using a diffusion plate or the like.

[0053] The reciprocating stage 16 also has an imaging optical system 24 and a sensor 26 inside. The imaging optical system 24 is composed of a reflector and an imaging lens, and makes the diffuse reflection light and the mirror reflection light from the object 12 form an image on the sensor 26. The sensor 26 receives the diffuse reflection light and the mirror reflection light formed by the imaging optical system 24, and generates an image signal corresponding to the received light. The sensor 26 is composed of a light receiving element such as a CCD (Charge Coupled Device) linear image sensor or a CMOS (Complementary Metal Oxide Semiconductor) image sensor, and converts the received light into a signal indicating its intensity. In addition, the sensor 26 has a color filter and generates image information indicating the color of the object 12. The sensor 26 outputs the diffuse reflection image information obtained by receiving the diffuse reflection light and the mirror reflection image information obtained by receiving the mirror reflection light to an external device, etc.

[0054] A conventional image reading device has a structure in which diffusely reflected light from the object 12 is read by irradiating light from the light source 18 or the light source 20 at an incident angle of 45° with respect to the normal line of the object 12. On the other hand, the texture reading device 10 of the present embodiment has a structure in which, in addition to the above structure, specularly reflected light from the object 12 is read by irradiating light from the light source 22 at an incident angle of 10° with respect to the normal line direction of the object 12.

[0055] exist Figure 1In the texture reading device 10 shown, the incident angle and the light receiving angle are constant in each pixel of the two-dimensional plane. Therefore, by calculating the difference between the image obtained at the light source incident angle of 45° as the diffuse reflection condition and the image obtained at the light source incident angle of 10° as the specular reflection condition, the metallic gloss information can be accurately extracted. That is, the metallic gloss area and the specular reflectivity of the two-dimensional plane can be obtained at one time by a simple difference calculation. In addition, by calibrating the diffuse reflection condition and the specular reflection condition with the same white calibration plate, the gloss information can be extracted by a simple difference calculation.

[0056] (First embodiment)

[0057] Next, the image processing device according to this embodiment will be described. Figure 2 It is a block diagram showing the structure of the image processing device involved in this embodiment.

[0058] The image processing device 30 according to this embodiment obtains Figure 1 The texture reading device 10 shown in the figure obtains the diffuse reflection image and the mirror reflection image and processes them, and outputs information indicating the glossiness of the metal on the surface of the object 12. In the present embodiment, as a differential operation, an example of subtracting the diffuse reflection image from the mirror reflection image is used for explanation. When the difference value of R (red), G (green), B (blue) or the difference value of L* (brightness) is positive (greater than 0), it is determined that the surface of the object is glossy, and the glossiness of the metal is determined in the surface of the object determined to be glossy. In the block diagram and flow chart described below, it is a prerequisite to determine that the surface of the object is glossy.

[0059] Here, the gloss of the surface of an object includes metallic gloss colored by specular reflection and non-metallic gloss not colored by specular reflection. The appearance of gloss observed in metallic gloss and non-metallic gloss such as resin other than metal is different. Specifically, in the case of metal, when irradiated with illumination light, light whose color of illumination light changes according to the color of the metal or the color of the metal-containing layer existing on the metal surface can be observed. On the other hand, in the case of non-metal, when irradiated with illumination light, the color of illumination light can be mainly observed. The gloss degree of metal indicates the degree of metallic gloss, and the image processing device 30 involved in this embodiment derives the gloss degree of metal.

[0060] The image processing device 30 has the functions of a diffuse reflection image acquisition unit 32, a specular reflection image acquisition unit 34, an RGB→L*C*H* conversion unit 36, a chromaticity difference image acquisition unit 38, and a metallic gloss degree determination unit 40. The image processing device 30 is composed of a computer such as a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory) connected to each other via a bus in a manner that allows communication. The CPU is a central processing unit that executes various programs or controls various units. That is, the CPU reads the program from the ROM and executes the program using the RAM as a work area. The CPU controls the above-mentioned structures and performs various calculations according to the program recorded in the ROM. In this embodiment, the ROM stores a program for executing the above-mentioned functions. In addition, the diffuse reflection image acquisition unit 32 and the specular reflection image acquisition unit 34 correspond to the acquisition unit, and the RGB→L*C*H* conversion unit 36, the chromaticity difference image acquisition unit 38, and the metallic gloss degree determination unit 40 correspond to the output unit.

[0061] The diffuse reflection image acquisition unit 32 and the specular reflection image acquisition unit 34 respectively acquire the diffuse reflection image and the specular reflection image obtained by the texture reading device 10. The diffuse reflection image acquisition unit 32 and the specular reflection image acquisition unit 34 may be respectively connected to the texture reading device 10 to acquire these images from the texture reading device 10. Alternatively, these images may be acquired by a server or the like connected to the texture reading device 10 via a network.

[0062] The RGB→L*C*H* conversion unit 36 ​​converts the diffuse reflection image acquired by the diffuse reflection image acquisition unit 32 and the specular reflection image acquired by the specular reflection image acquisition unit 34 from image data of color components of R (red), G (green), and B (blue) to image data of color components of L* (brightness), C* (chroma), and H* (hue angle). In addition, as a conversion method, a known color conversion technology is applied and changed, so a detailed description is omitted.

[0063] The chromaticity difference image acquisition unit 38 acquires a chromaticity difference image using the image data of the diffuse reflection image and the mirror reflection image converted by the RGB→L*C*H* conversion unit 36. That is, the chromaticity difference image is acquired by obtaining a difference image of the color component of C*. As the chromaticity difference image, there are a case where the diffuse reflection image is subtracted from the mirror reflection image and a case where the mirror reflection image is subtracted from the diffuse reflection image. Either case may be used, but in this embodiment, an example of subtracting the diffuse reflection image from the mirror reflection image is used for explanation.

[0064] The metallic glossiness determination unit 40 determines the glossiness of the metal using the chroma difference image acquired by the chroma difference image acquisition unit 38, and outputs the determination result. In the present embodiment, the magnitude of the chroma difference is output as the object surface information indicating the glossiness of the metal. Furthermore, when the chroma difference is positive (when the specular reflection image is larger than the diffuse reflection image), the metallic glossiness determination unit 40 determines that it is the glossiness of the metal, and when the chroma difference is 0 or negative, it is determined that it is the glossiness of the non-metal.

[0065] Figure 3 1 is a diagram for explaining an example of determination of the glossiness of a metal by the metallic glossiness determination unit 40 in this embodiment. Figure 3 In the figure, three types of metallic gloss levels, A to C, are compared and shown. In addition, the diffuse reflection images and specular reflection images of A to C are magenta-based images.

[0066] Regarding the difference image between the diffuse reflection image and the specular reflection image, in the RGB image, the lower the metallic glossiness, the stronger the complementary color green. And, in the C* (chroma) image, the higher the metallic glossiness, the larger the chroma difference image. Therefore, the metallic glossiness determination unit 40 uses the size of the chroma difference as the glossiness of the metal to determine the glossiness of the metal. In addition, the metallic glossiness determination unit 40 can also binarize the chroma difference image with an arbitrary set value to determine the presence or absence of the metallic glossiness. For example, as an example, when Figure 3 When the chroma difference (mirror reflection image - diffuse reflection image) is positive, it is judged to have metallic luster, and when it is negative, it is judged to have no metallic luster. In addition, when the chroma difference (diffuse reflection image - mirror reflection image) is negative, it is judged to have metallic luster, and when it is positive, it is judged to have no metallic luster.

[0067] Next, specific processing performed by the image processing device 30 according to the present embodiment having the above-described configuration will be described. Figure 4 This is a flowchart showing an example of the flow of processing performed by the image processing device 30 according to the present embodiment.

[0068] In step S100 , the diffuse reflection image acquisition unit 32 acquires a diffuse reflection image and the process moves to step S102 .

[0069] In step S102 , the specular reflection image acquisition unit 34 acquires a specular reflection image and the process moves to step S104 .

[0070] In step S104 , the RGB→L*C*H* conversion unit 36 ​​converts each of the diffuse reflection image and the specular reflection image from RGB images into image data of the color components of L*C*H*, and the process proceeds to step S106 .

[0071] In step S106, the saturation difference image acquisition unit 38 acquires a saturation difference image between the diffuse reflection image and the specular reflection image and moves to step S108. Here, an example is described in which a saturation difference image is acquired by subtracting the diffuse reflection image from the specular reflection image.

[0072] In step S108, the metallic gloss level determination unit 40 determines whether there is metallic gloss. Here, as an example, it is determined whether the difference image is positive (greater than 0). If the determination is affirmative, the process moves to step S110, and if it is negative, the process moves to step S112.

[0073] In step S110 , the metallic gloss degree determination unit 40 outputs the presence of metallic gloss as a determination result.

[0074] On the other hand, in step S112 , the metallic gloss degree determination unit 40 outputs the absence of metallic gloss as a determination result.

[0075] Thus, the determination result of the metallic gloss degree determination unit 40 is used to display or form an image corresponding to the presence or absence of metallic gloss, thereby reproducing the metallic gloss when the texture of the object surface is metallic gloss, and reproducing the non-metallic gloss when it is not metallic gloss.

[0076] (Second embodiment)

[0077] Next, the image processing device according to this embodiment will be described. Figure 5 1 is a block diagram showing the structure of the image processing device involved in this embodiment. Figure 2 The same structures are denoted by the same reference numerals and detailed descriptions thereof are omitted.

[0078] The image processing device 31 involved in this embodiment has the functions of a diffuse reflection image acquisition unit 32, a specular reflection image acquisition unit 34, a maximum differential chromaticity acquisition unit 35, a maximum differential chromaticity differential image acquisition unit 39, and a metallic gloss degree determination unit 40. The image processing device 31 is composed of a computer connected to each other via a bus in a manner that allows communication. The CPU is a central processing unit that executes various programs or controls various units. That is, the CPU reads the program from the ROM and executes the program using the RAM as a work area. The CPU controls the above-mentioned structures and performs various calculations according to the program recorded in the ROM. In this embodiment, the ROM stores programs for executing the above-mentioned functions. In addition, the maximum differential chromaticity acquisition unit 35, the maximum differential chromaticity differential image acquisition unit 39, and the metallic gloss degree determination unit 40 correspond to the output unit.

[0079] The diffuse reflection image acquisition unit 32 and the specular reflection image acquisition unit 34 respectively acquire the diffuse reflection image and the specular reflection image obtained by the texture reading device 10. The diffuse reflection image acquisition unit 32 and the specular reflection image acquisition unit 34 may be respectively connected to the texture reading device 10 to acquire these images from the texture reading device 10. Alternatively, these images may be acquired by a server or the like connected to the texture reading device 10 via a network.

[0080] The maximum difference chromaticity acquisition unit 35 extracts the maximum chromaticity with the largest value and the minimum chromaticity with the smallest value in the RGB colors from the diffuse reflection image acquired by the diffuse reflection image acquisition unit 32 as the maximum difference chromaticity, and notifies the maximum difference chromaticity difference image acquisition unit 39 of the extraction result.

[0081] The maximum difference chromaticity difference image acquisition unit 39 acquires difference images for the maximum difference chromaticity of each of the diffuse reflection image and the specular reflection image based on the acquisition result of the maximum difference chromaticity acquisition unit 35. The chromaticity is determined by the value (difference) obtained by subtracting the value of the smallest color from the value of the largest color in RGB, so the maximum difference chromaticity difference images of each of the diffuse reflection image and the specular reflection image are equivalent to the chromaticity difference image in the first embodiment.

[0082] The metallic glossiness determination unit 40 determines the glossiness of the metal. In this embodiment, the metallic glossiness determination unit 40 determines the glossiness of the metal using the maximum differential chromaticity difference image acquired by the maximum differential chromaticity difference image acquisition unit 39, and outputs the determination result. For example, the maximum differential chromaticity difference (e.g., Figure 6The R value - G value) is output as information indicating the degree of metallic gloss.

[0083] Figure 6 This is a diagram for explaining an example of determination of the glossiness of metal by the metallic glossiness determination unit 40 in the present embodiment.

[0084] exist Figure 6 In, with Figure 3 Similarly, as the degree of metallic gloss, three types of A to C are compared and shown. In addition, the diffuse reflection images and specular reflection images of A to C are magenta-based images.

[0085] exist Figure 6 In the example of , as enclosed by the dotted line, the maximum difference chromaticity acquisition unit 35 acquires R color as the maximum color and G color as the minimum color from the RGB image of the diffuse reflection image.

[0086] As enclosed by a single-dot chain line, the maximum differential chromaticity difference image acquisition unit 39 acquires a difference image between a diffuse reflection image and a specular reflection image for the R color and the G color of the maximum differential chromaticity. Figure 6 In the example of FIG. 1 , an example of subtracting a diffuse reflection image from a specular reflection image with respect to maximum differential chromaticity is shown.

[0087] Then, the metallic gloss level determination unit 40 subtracts the G color from the R color of the difference image corresponding to the chroma, and outputs the subtracted G color as information indicating the metallic gloss level, similarly to the first embodiment.

[0088] Next, specific processing performed by the image processing device 31 according to the present embodiment having the above-described configuration will be described. Figure 7 1 is a flowchart showing an example of the flow of processing performed by the image processing device 31 according to the present embodiment. In addition, the same processing as that of the first embodiment is denoted by the same reference numerals and described.

[0089] In step S100 , the diffuse reflection image acquisition unit 32 acquires a diffuse reflection image and the process moves to step S102 .

[0090] In step S102 , the specular reflection image acquisition unit 34 acquires a specular reflection image and the process moves to step S103 .

[0091] In step S103 , the maximum difference chromaticity acquisition unit 35 acquires the maximum color and the minimum color of RGB colors from the diffuse reflection image acquired by the diffuse reflection image acquisition unit 32 as the maximum difference chromaticity, and the process proceeds to step S105 .

[0092] In step S105, the maximum differential chromaticity difference image acquisition unit 39 acquires a differential image for the maximum differential chromaticity of each of the diffuse reflection image and the specular reflection image, and the process moves to step S107. Here, an example is described in which a differential image is acquired by subtracting the diffuse reflection image from the specular reflection image for the maximum differential chromaticity.

[0093] In step S107, the metallic gloss level determination unit 40 determines whether there is metallic gloss. For example, since the difference image of the maximum differential chromaticity corresponds to the saturation, the determination of whether there is metallic gloss is made in the same manner as in the first embodiment. If the determination is affirmative, the process proceeds to step S110, and if it is negative, the process proceeds to step S112.

[0094] In step S110 , the metallic gloss degree determination unit 40 outputs the presence of metallic gloss as a determination result.

[0095] On the other hand, in step S112 , the metallic gloss degree determination unit 40 outputs the absence of metallic gloss as a determination result.

[0096] Thus, the determination result of the metallic gloss degree determination unit 40 is used to display or form an image corresponding to the presence or absence of metallic gloss, thereby reproducing the metallic gloss when the texture of the object surface is metallic gloss, and reproducing the non-metallic gloss when it is not metallic gloss.

[0097] (Third embodiment)

[0098] Next, a display device according to this embodiment will be described. Figure 8 1 is a block diagram showing the structure of the display device according to the present embodiment. In addition, the display device 50 according to the present embodiment includes the image processing device 30 according to the first embodiment, and therefore, the display device 50 may be used in conjunction with the image processing device 30 according to the first embodiment. Figure 2 The same structures are denoted by the same reference numerals and detailed descriptions thereof are omitted.

[0099] The display device 50 according to the present embodiment is configured to include the image processing device 30 according to the first embodiment. Specifically, the display device 50 has the functions of a diffuse reflection image acquisition unit 32, a specular reflection image acquisition unit 34, an RGB→L*C*H* conversion unit 36, a chromaticity difference image acquisition unit 38, and a metallic glossiness determination unit 40.

[0100] Furthermore, the display device 50 has functions of a difference image acquisition unit 42 as an image acquisition unit, a diffuse reflection distribution function calculation unit 44, a specular reflection distribution function calculation unit 46, a reflection distribution function calculation unit 52, a parameter adjustment unit 54, a light source information acquisition unit 56, a camera information acquisition unit 58, a conceptual drawing unit 60, and a display unit 62. In addition, the conceptual drawing unit 60 and the display unit 62 correspond to a reproduction unit.

[0101] When the metallic gloss level determination unit 40 determines that there is metallic gloss, the differential image acquisition unit 42 calculates the difference between the diffuse reflection image and the specular reflection image in the RGB image with color components to obtain a differential image. Furthermore, when the metallic gloss level determination unit 40 determines that there is no metallic gloss, the differential image acquisition unit 42 converts the RGB image into grayscale (GrayScale) and calculates the difference between the diffuse reflection image and the specular reflection image in the grayscale without color components to obtain a differential image. For example, the conversion from RGB to grayscale is performed by the following formula (1).

[0102] Grayscale = ((0.298912*R)+(0.586611*G)+(0.114478*B))……(1)

[0103] In addition, there are two difference images, namely, (specular reflection image-diffuse reflection image) and (diffuse reflection image-specular reflection image), and the difference image acquisition unit 42 performs calculations on at least one of these difference images.

[0104] The diffuse reflection distribution function calculation unit 44 uses the diffuse reflection image to calculate the diffuse reflection distribution function of the object 12. For example, the diffuse reflection distribution function calculation unit 44 calculates the diffuse reflection rate ρd as a parameter based on the diffuse reflection image, by setting ρd as the diffuse reflection rate relative to the incident light, θi as the incident angle, and the diffuse reflection distribution function as ρd·cosθi according to the Lambert reflection model.

[0105] When there is metallic gloss, the specular reflection distribution function calculation unit 46 uses the RGB differential image to calculate the specular reflection distribution function of the object 12. When there is no metallic gloss, the specular reflection distribution function of the object is calculated using the grayscale differential image. For example, the specular reflection distribution function calculation unit 46 sets ρs as the specular reflectivity, γ as the angle between the specular reflection direction and the line of sight, n as the specular reflection index, and the specular reflection distribution function as ρs·cosnγ according to the Phong reflection model, and calculates the specular reflectivity ρs and n as parameters based on the differential image. In addition, two differential images are acquired by the differential image acquisition unit 42. When these two differential images are used to calculate the mirror reflection distribution function, the mirror reflection distribution function calculation unit sets the mirror reflection distribution function to ρs1·cosn1γ with respect to the differential image (mirror reflection image-diffuse reflection image), and sets the mirror reflection distribution function to ρs2·cosn2γ with respect to the differential image (diffuse reflection image-mirror reflection image), and calculates ρs1, ρs2, n1, and n2 as parameters based on the respective differential images.

[0106] The reflection distribution function calculation unit 52 calculates the reflection distribution function of each pixel of the object 12 using the diffuse reflection distribution function calculated by the diffuse reflection distribution function calculation unit 44 and the specular reflection distribution function calculated by the specular reflection distribution function calculation unit 46. For example, the reflection distribution function calculation unit 52 calculates the reflection distribution function according to the Lambert reflection model and the Phong reflection model through reflection distribution function = diffuse reflection distribution function + specular reflection distribution function.

[0107] The parameter adjustment unit 54 sets various parameters, the light source information acquisition unit 56 acquires light source information such as the light source direction, and the camera information acquisition unit 58 acquires camera information such as the line of sight direction. For example, in the parameter adjustment unit 54, the specular reflection distribution function calculation unit 46 sets the parameters of the reflection model used when calculating the specular reflectance based on the differential image and the specular reflection weighting coefficient. Specifically, the parameter adjustment unit 54 sets the parameters such as the diffuse reflection weighting coefficient wd, the specular reflection weighting coefficient ws, and the specular reflection index n used in the reflection model to pre-set fixed values ​​according to the reflection characteristics of the target object or the output characteristics of the display device.

[0108] The concept drawing unit 60 performs concept drawing processing on the three-dimensional model on a virtual screen set in a virtual three-dimensional space based on the reflection distribution function calculated by the reflection distribution function calculation unit 52, the various parameters set by the parameter adjustment unit 54, the light source information (light source direction) obtained by the light source information acquisition unit 56, and the camera information (line of sight direction) obtained by the camera information acquisition unit 58. In addition, the concept drawing processing is well known, and for example, the concept drawing processing can be performed using the radiosity method or the ray tracing method taking into account mutual reflection.

[0109] The display unit 62 uses the image data obtained by the conceptual drawing processing to reproduce and display the texture of the object 12 in CG. By reproducing and displaying the texture of the object 12 in this way, the object surface is reproduced and displayed in a manner that the higher the chroma of the diffuse reflection image is than the mirror reflection image, the stronger the chroma of the illumination is. In addition, the texture of the object 12 can also be reproduced and displayed in CG by performing the determination of the metallic gloss degree determination unit 40 using the second embodiment. In this case, the object surface is reproduced in a manner that the higher the value representing the chroma of the diffuse reflection image is than the mirror reflection image, the stronger the chroma of the illumination is.

[0110] Next, specific processing performed by the display device 50 according to the present embodiment configured as described above will be described. Fig. 9 1 is a flowchart showing an example of the flow of processing performed by the display device 50 according to the present embodiment. In addition, the same processing as that performed by the image processing device 30 according to the first embodiment is denoted by the same reference numerals and described.

[0111] In step S100 , the diffuse reflection image acquisition unit 32 acquires a diffuse reflection image and the process moves to step S102 .

[0112] In step S102 , the specular reflection image acquisition unit 34 acquires a specular reflection image and the process moves to step S104 .

[0113] In step S104 , the RGB→L*C*H* conversion unit 36 ​​converts each of the diffuse reflection image and the specular reflection image from RGB images into image data of the color components of L*C*H*, and the process proceeds to step S106 .

[0114] In step S106, the saturation difference image acquisition unit 38 acquires a saturation difference image between the diffuse reflection image and the specular reflection image and moves to step S108. Here, an example is described in which a saturation difference image is acquired by subtracting the diffuse reflection image from the specular reflection image.

[0115] In step S108, the metallic gloss level determination unit 40 determines whether there is metallic gloss. Here, as an example, it is determined whether the difference image is positive (greater than 0). If the determination is affirmative, the process moves to step S109, and if it is negative, the process moves to step S111.

[0116] In step S109, the difference image acquisition unit 42 acquires a difference image having color components and moves to step S114. That is, the difference between the diffuse reflection image and the specular reflection image in the RGB image having color components is calculated to acquire the difference image.

[0117] On the other hand, in step S111, the difference image acquisition unit 42 acquires a difference image without color components and transfers to step S114. That is, the difference image acquisition unit 42 converts the RGB images of the diffuse reflection image and the specular reflection image into gray scale, and calculates the difference between the diffuse reflection image and the specular reflection image in the gray scale without color components to acquire the difference image.

[0118] In step S114, the diffuse reflection distribution function calculation unit 44 uses the diffuse reflection image to calculate the diffuse reflection distribution function of the object 12 and transfers to step S116. Specifically, the diffuse reflectance is calculated based on the diffuse reflection image and the diffuse reflection weighting coefficient. In addition, regarding the diffusion weighting coefficient, a fixed value is preset by the parameter adjustment unit 54 according to the reflection characteristics of the target object or the output characteristics of the display device.

[0119] In step S116, the specular reflection distribution function calculation unit 46 uses the differential image acquired by the differential image acquisition unit 42 to calculate the specular reflection distribution function of the object 12 and transfers to step S118. Specifically, the specular reflectivity is calculated based on the differential image and the specular reflection weighting coefficient. In addition, regarding the specular reflection weighting coefficient, a fixed value is pre-set by the parameter adjustment unit 54 according to the reflection characteristics of the target object or the output characteristics of the display device. In addition, in the present embodiment, when calculating the specular reflection distribution function, the RGB differential image is used to calculate the specular reflection distribution function of the object 12 when there is metallic gloss, and the grayscale differential image is used to calculate the specular reflection distribution function of the object when there is no metallic gloss.

[0120] In step S118, the reflection distribution function calculation unit 52 uses the diffuse reflection distribution function calculated in step S114 and the specular reflection distribution function calculated in step S116 to calculate the reflection distribution function of each pixel of the object 12 and transfers to step S120. Specifically, the reflection distribution function calculation unit 52 uses the diffuse reflection distribution function obtained by the diffuse reflection distribution function calculation unit 44 and the specular reflection distribution function obtained by the specular reflection distribution function calculation unit 46, according to the Lambert reflection model and the Phong reflection model,

[0121] [Mathematical formula 1]

[0122] I(x, y) = I i ·{w d ·G d (x, y)·cosθ i}+I i ·{w s ·{G s (x, y)-G d (x, y)}·cos n γ}

[0123] The reflected light intensity I(x, y) on the two-dimensional plane is calculated as the reflection distribution function by the above formula.

[0124] in,

[0125] {wd·Gd(x,y)·cosθi}: diffuse reflection distribution function

[0126] wd: diffuse reflection weighting coefficient

[0127] Gd(x, y): diffuse reflection image

[0128] {ws·{Gs(x, y)-Gd(x, y)}·cosnγ}: specular reflection distribution function

[0129] ws: Specular reflection weighting factor

[0130] Gs(x, y): Specular reflection image

[0131] n: Specular reflection index.

[0132] In addition, regarding the specular reflection index, a fixed value is preset by the parameter adjustment unit 54 according to the reflection characteristics of the target object or the output characteristics of the display device. In addition, the difference image is (specular reflection image-diffuse reflection image), and the pixels with negative difference values ​​become 0. The reflected light intensity I (x, y) is calculated for each of the R, G, and B components of the diffuse reflection image and the specular reflection image. In this way, the reflected color of the object 12 is calculated.

[0133] In step S120, the display unit 62 displays an image that reproduces the texture of the surface of the object and ends a series of processing. That is, the concept drawing unit 60 performs concept drawing processing on the three-dimensional model on the virtual screen set in the virtual three-dimensional space based on the reflection distribution function calculated in step S116, the various parameters set by the parameter adjustment unit 54, the light source information (light source direction) obtained by the light source information acquisition unit 56, and the camera information (line of sight direction) obtained by the camera information acquisition unit 58. In addition, the display unit 62 reproduces and displays the texture of the object 12 in CG using the image data obtained by the concept drawing processing.

[0134] Thus, in this embodiment, the diffuse reflection distribution function is calculated based on the diffuse reflection image, and the specular reflection distribution function is calculated based on the differential image. Here, since only the glossy part of the object is accurately extracted from the differential image, the specular reflection distribution function of each pixel in the two-dimensional plane can be calculated with high precision. Thus, the texture of the surface of the object can be displayed by a simple calculation using less image data without obtaining the bidirectional reflection distribution function (BRDF). Furthermore, since the presence or absence of metallic gloss is determined based on the differential image of chroma, and the reflection distribution function corresponding to the presence or absence of metallic gloss is calculated to display the image, the metallic gloss is reproduced when the texture of the surface of the object is metallic gloss, and the non-metallic gloss is reproduced when it is not metallic gloss.

[0135] (Fourth embodiment)

[0136] Next, the image forming apparatus according to the present embodiment will be described. Fig.10 2 is a block diagram showing the structure of the image forming apparatus according to the present embodiment. In addition, the image forming apparatus 70 according to the present embodiment also includes the image processing apparatus 30 according to the first embodiment, so sometimes the image forming apparatus 70 and the image processing apparatus 30 are interchangeable. Figure 2 and Figure 8 The same structures are denoted by the same reference numerals and detailed descriptions thereof are omitted.

[0137] The image forming device 70 involved in this embodiment also includes the image processing device 30 involved in the first embodiment, and has the functions of a diffuse reflection image acquisition unit 32, a specular reflection image acquisition unit 34, an RGB→L*C*H* conversion unit 36, a chromaticity difference image acquisition unit 38 and a metallic gloss degree determination unit 40.

[0138] The image forming device 70 has the functions of the difference image acquisition unit 42 as an image acquisition unit, the data conversion unit 64 as a conversion unit, and the imaging unit 66. The data conversion unit 64 and the imaging unit 66 correspond to a reproduction unit.

[0139] When the metallic gloss level determination unit 40 determines that there is metallic gloss, the differential image acquisition unit 42 calculates the difference between the diffuse reflection image and the mirror reflection image in the RGB image with color components in the same manner as in the third embodiment to obtain a differential image. Furthermore, when the metallic gloss level determination unit 40 determines that there is no metallic gloss, the differential image acquisition unit 42 converts the RGB image into grayscale and calculates the difference between the diffuse reflection image and the mirror reflection image in the grayscale without color components to obtain a differential image. In addition, there are two differential images, namely (mirror reflection image-diffuse reflection image) and (diffuse reflection image-mirror reflection image), and the differential image acquisition unit 42 calculates at least one of these differential images.

[0140] When the metallic gloss degree determination unit 40 determines that there is metallic gloss, the data conversion unit 64 converts the diffuse reflection image (R, G, B) and the differential image (ΔR, ΔG, ΔB) of RGB with color components into recording material data containing metallic components (for example, C (blue), M (magenta), Y (yellow), K (black), Si (silver recording material)). And, when the metallic gloss degree determination unit 40 determines that there is no metallic gloss, the differential image (ΔGray) of the diffuse reflection image (R, G, B) and the grayscale without color components is converted into recording material data not containing metallic components (for example, C, M, Y, K, Ct (colorless recording material)). The data conversion unit 64 uses a gloss texture reproduction file to perform data conversion. The gloss texture reproduction file is generated by obtaining the correspondence between the color space of the recording material data (C, M, Y, K, Si) and the color space of the texture information (R, G, B, ΔR, ΔG, ΔB) or (R, G, B, ΔGray). As an example, the correspondence is generated using the technology described in Japanese application No. 2018-189223 filed by the present application. In addition, the silver recording material may be replaced by a metal film or metal paper instead of metal ink or metal toner. Furthermore, RGB may be converted to L*a*b*, and ΔGray may be converted to Gloss.

[0141] The imaging unit 66 forms an image on the recording medium using the recording material data converted by the data conversion unit 64. By forming an image that reproduces the texture of the object 12 in this way, an image that reproduces the surface of the object is formed in a manner that the higher the chroma of the diffuse reflection image is than the mirror reflection image, the stronger the chroma of the illumination is. Alternatively, the second embodiment may be used to perform the determination of the metallic gloss degree determination unit 40 to form an image that reproduces the texture of the object 12. In this case, an image that reproduces the surface of the object is formed in a manner that the higher the value representing the chroma of the diffuse reflection image is than the mirror reflection image, the stronger the chroma of the illumination is.

[0142] Next, specific processing performed by the image forming apparatus 70 according to the present embodiment having the above-described configuration will be described. Fig.11 1 is a flowchart showing an example of the flow of processing performed by the image forming apparatus 70 according to the present embodiment. In addition, the same processing as that performed by the image processing apparatus 30 according to the first embodiment is denoted by the same reference numerals and described.

[0143] In step S100 , the diffuse reflection image acquisition unit 32 acquires a diffuse reflection image and the process moves to step S102 .

[0144] In step S102 , the specular reflection image acquisition unit 34 acquires a specular reflection image and the process moves to step S104 .

[0145] In step S104 , the RGB→L*C*H* conversion unit 36 ​​converts each of the diffuse reflection image and the specular reflection image from RGB images into image data of the color components of L*C*H*, and the process proceeds to step S106 .

[0146] In step S106, the saturation difference image acquisition unit 38 acquires a saturation difference image between the diffuse reflection image and the specular reflection image and moves to step S108. Here, an example is described in which a saturation difference image is acquired by subtracting the diffuse reflection image from the specular reflection image.

[0147] In step S108, the metallic gloss level determination unit 40 determines whether there is metallic gloss. Here, as an example, it is determined whether the difference image is positive (greater than 0). If the determination is affirmative, the process moves to step S109, and if it is negative, the process moves to step S111.

[0148] In step S109, the difference image acquisition unit 42 acquires a difference image having color components and moves to step S113. That is, the difference between the diffuse reflection image and the specular reflection image in the RGB image having color components is calculated to acquire the difference image.

[0149] On the other hand, in step S111, the difference image acquisition unit 42 acquires a difference image without color components and transfers to step S113. That is, the difference image acquisition unit 42 converts the RGB images of the diffuse reflection image and the specular reflection image into gray scale, and calculates the difference between the diffuse reflection image and the specular reflection image in the gray scale without color components to acquire the difference image.

[0150] In step S113, the data conversion unit 64 converts the data from the RGB color space to the color space of the recording material data and transfers to step S115. That is, when a differential image with color components is obtained in step S109, (R, G, B, ΔR, ΔG, ΔB) is converted to (C, M, Y, K, Si). And when a differential image without color components is obtained in step S111, (R, G, B, ΔGray) is converted to (C, M, Y, K, Ct).

[0151] In step S115 , the image forming section 66 forms an image on a recording medium such as paper using the converted recording material data and ends a series of processing.

[0152] By performing such processing, in this embodiment, as in the above-mentioned embodiments, the texture of the surface of the object can be displayed by a simple calculation using a small amount of image data without obtaining a bidirectional reflectance distribution function (BRDF). In addition, since the presence or absence of metallic gloss is determined based on the difference image of chroma, and the difference image corresponding to the presence or absence of metallic gloss is converted into recording material data to form an image, the metallic gloss is reproduced when the texture of the surface of the object is metallic gloss, and the non-metallic gloss is reproduced when it is not metallic gloss.

[0153] In addition, the processing performed by the image processing devices 30, 31, the display device 50, and the image forming device 70 involved in the above-mentioned embodiment may be set as processing performed by software, or may be set as processing performed by hardware, or may be set as processing composed of a combination of the two. Furthermore, the processing performed by the image processing devices 30, 31, the display device 50, and the image forming device 70 may be stored in a storage medium in the form of a program and circulated.

[0154] Furthermore, the present invention is not limited to the above, and can of course be implemented with various modifications other than the above without departing from the spirit and scope of the present invention.

[0155] The above-mentioned embodiments of the present invention are provided for the purpose of illustration and description. In addition, the embodiments of the present invention do not fully and exhaustively include the present invention, and do not limit the present invention to the disclosed methods. Obviously, various modifications and changes are self-evident to those skilled in the art to which the present invention belongs. The present embodiment is selected and described in order to most easily understand the principles of the present invention and its application. Thus, other technicians in the art can understand the present invention through various variations optimized for specific uses assumed to be various embodiments. The scope of the present invention is defined by the above claims and their equivalents.

Claims

1. An image processing device, comprising: An acquisition unit, which acquires a diffuse reflection image of the object surface and a specular reflection image of the object surface; and The reproducing unit reproduces the object surface using the diffuse reflection image and the specular reflection image in such a manner that the higher the chroma of the diffuse reflection image or the value indicating the chroma is than that of the specular reflection image, the stronger the chroma of the illumination.

2. An image processing device, comprising: An acquisition unit, which acquires a diffuse reflection image of the object surface and a specular reflection image of the object surface; and The output unit outputs object surface information using the diffuse reflection image and the specular reflection image, wherein the higher the chroma of the specular reflection image or the value representing the chroma is than that of the diffuse reflection image, the higher the glossiness of the metal on the surface of the object is.

3. The image processing device according to claim 2, wherein: The output unit outputs a difference image between the chroma of the diffuse reflection image and the chroma of the specular reflection image as the object surface information.

4. The image processing device according to claim 2, wherein: The output unit extracts maximum chromaticity and minimum chromaticity in the diffuse reflection image acquired by the acquisition unit, and outputs a difference image between the diffuse reflection image and the specular reflection image of each of the extracted maximum chromaticity and minimum chromaticity as the object surface information.

5. The image processing device according to any one of claims 2 to 4, comprising: an image acquisition unit that selects the presence or absence of a color component using the object surface information output by the output unit, and acquires a difference image between the diffuse reflection image and the specular reflection image corresponding to the presence or absence of the selected color component; a reflection distribution function calculation unit, which calculates a reflection distribution function of the surface of the object using the diffuse reflection image acquired by the acquisition unit and the difference image acquired by the image acquisition unit; and The display unit displays a reflection color of the surface of the object corresponding to a change in the orientation of the surface of the object using the reflection distribution function calculated by the reflection distribution function calculation unit.

6. The image processing device according to claim 5, wherein: When the glossiness of the metal is higher than a preset glossiness of the metal, the image acquisition unit acquires a differential image with a color component, and when the glossiness of the metal is lower than the preset glossiness of the metal, the image acquisition unit acquires a differential image without a color component.

7. The image processing device according to any one of claims 2 to 4, further comprising: an image acquisition unit that selects the presence or absence of a color component using the object surface information output by the output unit, and acquires a difference image between the diffuse reflection image and the specular reflection image corresponding to the presence or absence of the selected color component; and A conversion section converts into recording material data using the diffuse reflection image acquired by the acquisition section and the difference image acquired by the image acquisition section.

8. The image processing device according to claim 7, wherein: When the glossiness of the metal is higher than a preset glossiness of the metal, the image acquisition unit acquires a differential image with a color component, and when the glossiness of the metal is lower than the preset glossiness of the metal, the image acquisition unit acquires a differential image without a color component. When the glossiness of the metal is higher than the preset glossiness of the metal, the conversion unit converts the diffuse reflection image acquired by the acquisition unit and the differential image with color components acquired by the image acquisition unit into recording material data containing metal components. When the glossiness of the metal is lower than the preset glossiness of the metal, the conversion unit converts the diffuse reflection image acquired by the acquisition unit and the differential image with no color components acquired by the image acquisition unit into recording material data not containing metal components. 9 . A storage medium storing an image processing program for causing a computer to function as each unit of the image processing apparatus according to claim 1 .

10. An image processing method comprising the following steps: an acquisition step, acquiring a diffuse reflection image of the object surface and a specular reflection image of the object surface; and The reproducing step uses the diffuse reflection image and the specular reflection image to reproduce the object surface in such a manner that the higher the chroma of the diffuse reflection image or the value representing the chroma is than that of the specular reflection image, the stronger the chroma of the illumination is.

11. An image processing method comprising the following steps: an acquisition step, acquiring a diffuse reflection image of the object surface and a specular reflection image of the object surface; and The output step uses the diffuse reflection image and the specular reflection image to output object surface information, wherein the higher the chroma of the specular reflection image or the value representing the chroma is than that of the diffuse reflection image, the higher the glossiness of the metal on the object surface is represented.

Images