JP2013038624A - Image processing method and image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To keep the balance of color arrangement from being destroyed when spectral reflection factors, etc. are calculated, and also, by taking the spectrum of reflected light or transmitted light of another object or the base color of paper into consideration, accurately calculate the spectral reflection factor of printed matter needed for color reproduction, thereby ensuring that images will be seen with the same impression even under different ambient light.SOLUTION: When, at the time the spectrum of light reflected on the object plane of a second object under a second light source (spectrum under the second light source) becomes the same as the spectrum of light reflected on the object plane of a first object under a first light source (spectrum under the first light source), a spectral reflection factor on the object plane of the second object (first spectral reflection factor) is calculated, cluster analysis about the spectrum under the first light source is performed and also cluster analysis about the spectrum of light reaching the object plane of the second object is performed. Then, by using these spectrums, the first spectral reflection factor is calculated.

Description

  In the case where an image printed on a sheet is printed on another sheet, the present invention enables each image to be printed even when printed on a sheet of a different type from the original sheet or when viewed in a different ambient light. Image processing method for performing color correction so that the images appear to have the same impression, and an image processing program for executing this image processing method, in particular, the color correction can be performed more accurately and the amount of calculation can be reduced The present invention relates to an image processing method and an image processing program.

An image forming apparatus typified by a printer, a copier, or a multifunction peripheral is desired to faithfully reproduce an input image and print it on a sheet.
However, in practice, these images may look different. The causes include, for example, a case where the type or position of ambient light is different, or a case where the type of paper is different.

  Here, the case where the type or position of the ambient light is different is specifically as follows. For example, it is assumed that a personal computer (PC) to which a scanner is connected is installed in the room a, and a printer (image forming apparatus) connected to the PC is installed in the room b. The type of light source in room a is different from the type of light source in room b. In this case, the impression when the image of the printed material read by the scanner is viewed in the room a may be different from the impression when the image of the printed material printed by the printer is viewed in the room b. This is because the type of light source in each room is different.

Thus, a technique has been proposed in which color correction is performed so that each image looks the same even if the type of light source is different.
For example, a spectral image photographing means for photographing the spectral information of the original image for each pixel, a photographing light spectrum detecting means for detecting the spectral distribution of illumination light in the space where the original image was photographed, and a space in which the image is reproduced Reproduction environment light spectrum detection means for detecting the spectrum distribution of the illumination light, means for calculating the spectral reflectance of the subject by removing the influence of the illumination light spectrum distribution from the spectrum information, and the reproduction environment light spectrum and the spectral reflectance distribution. A means for calculating a spectral distribution when the subject is photographed with the same illumination light as the reproduction area, a means for converting the spectral distribution of each pixel into a three-dimensional color vector, and a color to be colored based on the color vector information Some have an image reproducing means (see, for example, Patent Document 1).
According to this technology, color correction can be performed so that each image looks the same even if the type of light source is different.

The case where the paper types are different includes, for example, a difference between plain paper and recycled paper, a difference between white paper and paper with a color other than white.
Therefore, a technique has been proposed for reproducing the color of an image so that the original image and the printed image have the same impression even if the type of paper is different.
For example, in a color copying machine or the like, there is one that easily switches between gradation and color reproducibility according to newly used recording paper (see, for example, Patent Document 2). Specifically, the gradation and color reproducibility of standard paper is stored in advance, and for other paper, standard paper at signal strengths of around 0% (paper part) and other than 0% (image part). The difference in reproducibility is expressed based on the photometric quantity and the colorimetric value ratio.
According to this technique, even if the type of paper is different, the color of the image can be reproduced so as to have the same impression as the original image.

JP-A-9-172649 JP 2001-119594 A

However, the techniques described in Patent Documents 1 and 2 described above have the following problems.
For example, the technique described in Patent Document 1 calculates the spectral information of the original image, the spectral reflectance distribution, the three-dimensional color vector, and the like for each pixel. That is, even when pixels whose positions in the original image are adjacent to each other have similar spectral information, calculation processing is performed for each pixel. For this reason, there is a possibility that the balance of the color arrangement is lost.
In addition, the technology considers the spectrum distribution of illumination light, but considers the spectrum of light reflected or transmitted from other objects (obstacles) installed in the observation environment where the printed material is viewed. There wasn't. For this reason, the reflection spectrum of the image cannot be calculated accurately, and as a result, the impression when viewing each image is different.

Furthermore, although the technique described in Patent Document 2 can reproduce individual colors, it does not consider the paper color (background color). That is, the contrast effect (illusion) due to the difference in the background (background) was not considered. For this reason, when images are actually printed on different types of paper using the technique, the images may not look the same.
Moreover, the fact that the image looks different when viewed under different ambient light has not been considered.

  The present invention has been considered in view of the above circumstances, and while keeping the balance of the color arrangement in the printed image, the spectrum of light reflected or transmitted by other objects, the color of the paper (background color), and the like. Image processing method and image processing program apparatus for accurately calculating the spectral reflectance of a printed material necessary for color reproduction and performing color correction so that the image looks the same when viewed under different ambient light The purpose is to provide.

  In order to achieve this object, the image processing method of the present invention includes a first light source spectrum calculation step for calculating a spectrum of light reflected by the target surface of the first object under the first light source as a spectrum under the first light source. When the spectrum of light reflected by the target surface of the second object under the second light source is the second light source spectrum, the second light source spectrum is the same as the first light source spectrum. Using the first spectral reflectance calculation step for calculating the spectral reflectance of the target surface of the second object as the first spectral reflectance, and the data calculated based on the first spectral reflectance, the image to be printed on the printed matter A color correction step for performing color correction, the spectrum calculation step under the first light source is a process of calculating the spectrum under the first light source for each of a plurality of pixels on the target surface of the first object, Per pixel And performing a cluster analysis on the spectrum under the first light source to form a plurality of clusters, and the first spectral reflectance calculation step performs a cluster analysis on the spectrum of light reaching the target surface of the second object. And a process of calculating the first spectral reflectance using the cluster-analyzed spectrum and the cluster-analyzed spectrum under the first light source.

  Further, the image processing program of the present invention includes a reference spectrum acquisition step of acquiring, as a reference spectrum, a spectrum of light reflected from the observation target surface under the reference light source, and an observation target surface under the current light source based on the reference spectrum. Based on the spectrum of the current light source, which calculates the reflected light spectrum as the spectrum of the current light source, the cluster analysis process of clustering the current light source spectrum into multiple clusters, and the spectrum of the light from the observation environment light Thus, the observation environment light correction process for correcting the spectrum under the current light source is executed by the color correction processing apparatus.

  According to the image processing method and the image processing program of the present invention, not only the spectrum of the light source but also the spectrum of light reflected or transmitted by another object and the color of the paper (background color) are taken into account. Color correction can be performed. Therefore, it is possible to perform color reproduction that does not change the impression of color arrangement when printed on different papers and observed under a plurality of types of ambient light that exists at various positions.

Further, by using the similarity of cluster analysis for the spectrum of the observation object (printed material), correction can be made so as to maintain the balance of the color arrangement.
Furthermore, the cluster of the spectrum of the light emitted from the light source to the printed matter and the spectrum of the light that is reflected or transmitted through the obstacle and applied to the printed matter reduces the amount of calculation of the spectrum synthesis. it can.

It is a block diagram which shows the structure of a color correction processing apparatus. It is an image figure which shows the structure of an observation environment. It is a flowchart which shows the operation | movement procedure of the color correction processing apparatus in embodiment of this invention. It is a flowchart which shows the procedure of the correction calculation process of observation environment light. It is a flowchart which shows the procedure of an environmental light correction process. It is a flowchart which shows the procedure of reflected light and transmitted light calculation processing. It is a flowchart which shows the procedure of an output paper correction process. It is a graph which shows the waveform of spectrum data, and the decomposition | disassembly waveform which decomposed | disassembled this waveform. It is a figure which shows the shape information of a spectrum. It is an image figure which shows the cluster based on similarity. It is a figure which shows the place which combined the light beam which belongs to the same cluster. It is a figure which shows the relationship between the spectrum of a light source, and distance. It is a figure which shows the spectrum of the light reflected by the obstruction. It is a figure which shows the spectrum of the light which permeate | transmitted the obstruction. It is a figure which shows the spectrum of the light reflected from an observation target object by observation environment light.

Hereinafter, preferred embodiments of an image processing method and an image processing program according to the present invention will be described with reference to the drawings.
In this embodiment, the color correction processing apparatus that executes the image processing method and the image processing program of the present invention will be described first, and then the image processing method and the image processing program will be described.

[Color correction processing device]
First, an embodiment of a color correction processing apparatus will be described with reference to FIG.
FIG. 2 is a block diagram showing the configuration of the color correction processing apparatus.
Here, the outline of the configuration of the color correction processing apparatus will be described, and the details of the processing executed by each configuration will be described in “Image processing method” described later.

  As shown in the figure, the color correction processing apparatus 1 includes a data input unit 11, a document data storage unit 12, a spectral correction processing unit 13, a spectrum shape information calculation unit 14, a cluster analysis calculation unit 15, and a cluster. The analysis result storage unit 16, the panel 17, the environmental data storage unit 18, the ambient light spectral data storage unit 19, the arithmetic unit 20, and the spectral data storage unit 21 are provided.

The data input unit 11 is an input unit that acquires reference spectrum data (described later) and paper information from the outside. In the present embodiment, “spectrum” refers to a spectral spectrum. “Paper information” refers to the spectrum data of the background color of the output paper under a reference light source (eg, CIE standard light source D50).
The document data storage unit 12 is storage means for storing reference spectrum data, spectrum data under the current light source (described later), spectrum shape information (described later), and XYZ values.
The “XYZ value” is used to convert the corrected spectrum into XYZ when the image forming apparatus performs print output.

The spectral correction processing unit 13 is an arithmetic unit that performs processing for converting the reference spectrum data into spectrum data under the current light source, intensity correction calculation of the observation environment light spectrum, and the like.
The spectrum shape information calculation unit 14 is a calculation unit that calculates shape information (described later) of the spectrum of light.
The cluster analysis calculation unit 15 is a calculation unit that receives data and performs cluster analysis.
The cluster analysis result storage unit 16 is a storage unit that stores the result of cluster analysis.

The panel 17 is an input unit that receives information related to the observation environment (such as the type and arrangement of obstacles such as walls and windows) and information related to the type and position of the observation environment light, etc., by an input operation by the user.
The environment data storage unit 18 is a storage unit that stores various information input through the panel 17.
The ambient light spectral data storage unit 19 is a storage unit that stores the spectrum of the observation environment light. The spectrum of the observation ambient light can be stored in the ambient light spectral data storage unit 19 after being input through the panel 17.
The calculation unit 20 is a calculation unit that performs distance calculation and vector calculation between the observation environment light, the surrounding obstacle, and the observation target.
The spectroscopic data storage unit 21 is a storage unit that stores the spectrum of the observation environment light that has been subjected to intensity correction.

  The color correction processing apparatus 1 described above can be manufactured as a single apparatus including the configuration shown in FIG. 1, but can also be mounted in an image forming apparatus. Image forming apparatuses in which the color correction processing apparatus can be mounted include printers, copiers, and multifunction machines.

[Image processing method]
Next, the operation (image processing method) of the color correction processing apparatus of this embodiment will be described with reference to FIGS.
FIG. 2 is an image diagram showing the configuration of the observation environment. FIG. 3 is a flowchart showing an operation procedure of the color correction processing apparatus according to the present embodiment. FIG. 4 is a flowchart showing the procedure of the observation environment light correction calculation process. FIG. 5 is a flowchart showing the procedure of the ambient light correction process. FIG. 6 is a flowchart showing the procedure of the reflected light / transmitted light calculation process. FIG. 7 is a flowchart illustrating a procedure of output sheet correction processing.

Here, the following items will be described.
(1) Observation environment configuration (2) Color correction processing (3) Observation environment light correction calculation processing (4) Reflected / transmitted light calculation processing (5) Output paper correction processing

(1) Configuration of Observation Environment In order to facilitate understanding of the contents of the processes described in (2) to (5), the configuration of the observation environment will be described first with reference to FIG.
The observation environment refers to, for example, a space where the paper is present when the user views an image printed on the paper by the image forming apparatus. Specifically, it corresponds to the room b described in [Background Art].
However, the image forming apparatus does not need to be installed in the observation environment. That is, when the printed material is viewed in a room in which the image forming apparatus is installed, the room is an observation environment. However, when the printed material is viewed in a room in which the image forming apparatus is not installed, the room is in the observation environment. It becomes. Here, for ease of explanation, it is assumed that no image forming apparatus is installed in the observation environment.

In the observation environment, one or more light sources S (in the figure, two light sources S1 and S2) are arranged.
Observation environment light L (L1, L2) is emitted from the light sources S (S1, S2). A vector indicating the direction in which the observation environment light L (L1, L2) propagates is defined as an environment light vector L → (L1 →, L2 →).

In addition, the notation of a vector is originally a symbol such as an arrow (→), a dot (•), an overbar (−), etc. on a character (for example, L), and the character is a vector. Is shown. However, in this document, since it is not possible to write an arrow or the like on a character, an arrow (→) is written on the right side of the character to represent a vector. This notation is the same for the inner product.
In the following description, the observation environment light L may be simply referred to as environment light L.

In the observation environment, the observation object O exists. Specifically, the observation object O is a sheet that the user is looking at.
In the observation object O, the surface that the user is viewing is referred to as an observation target surface P. A distance between the light source S and the observation target surface P when the observation environment light L is irradiated from the light source S to the observation target surface P is defined as a distance rO.
Further, a vector of light propagating from the observation target plane P in the orthogonal direction is defined as a normal vector n →.

Furthermore, in the observation environment, there may be an object other than the observation object O. Examples of objects other than the observation object O include walls and windows. These are called surrounding obstacles (or simply obstacles).
When the observation environment light L from the light source S is applied to the surrounding obstacle, the intersection point of the surrounding obstacle with the light beam of the observation environment light L is the intersection point B, and the distance between the light source S and the intersection point B is the distance rB. .
However, when the light irradiated to the intersection point B is not the observation environment light L itself from the light source S but light reflected or transmitted by another surrounding obstacle (another intersection point B ′), the intersection point B ′. And a distance between the intersection B and the distance rB.

A vector indicating the propagation direction of the light reflected at the intersection B is defined as a reflected light vector LR →. Further, a vector indicating the propagation direction of the light transmitted through the intersection B is defined as a transmitted light vector LT →.
In the figure, the transmitted light vector LT → is directed outward from the observation environment and is not irradiated on the observation object O. However, the present invention is not limited to this. When there is a surrounding obstacle between the observation object O and the observation object O, the transmitted light vector LT → from the surrounding obstacle may be irradiated to the observation object O.

  The angle between the light of the observation environment light L when the observation environment light L is incident on the intersection B and the surrounding obstacle surface is defined as θ. This angle θ is the same as the reflection angle of the reflected light vector LR →, that is, the angle between the surrounding obstacle surface and the reflected light vector LR →. The angle θ is the same as the transmission angle of the transmitted light vector LT → that is, the angle between the surrounding obstacle surface and the transmitted light vector LT →.

(2) Color Correction Processing Color correction processing is a central processing procedure in the image processing method of this embodiment.
The color correction processing procedure will be described with reference to FIG.

As shown in the figure, the data input unit 11 of the color correction processing apparatus 1 shows a spectrum of light reflected on the observation target surface P (paper base) under a reference light source (for example, CIE standard light source D50). Data (reference spectrum data) is acquired from the outside (reference spectrum acquisition step, step 10).
Specifically, an image reading device (not shown) such as a scanner is prepared, and this image reading device reads an image printed on one surface (observation target surface P) of a sheet (observation target O), Based on the data relating to the read image, the reference spectrum data is calculated (or the spectroscope provided in the image reading device detects the spectrum of the light reflected by the observation target surface P of the paper, and the reference spectrum data is obtained. And the data input unit 11 acquires the reference spectrum data.

The image reading apparatus is an apparatus capable of reading an image with a predetermined resolution (for example, 600 [dpi]). When the observation target surface P is divided into a predetermined number of pixels, each pixel (one dot) ) To calculate the reference spectrum data. For example, when the paper is A4 size, the observation target surface P of the paper is divided into 7128 × 5040 dots, and the reference spectrum data can be calculated for each dot.
The reference spectrum data is composed of data indicating the wavelength and data indicating the intensity of the spectrum at the wavelength. The same applies to spectrum data under current light source, spectrum data of observation environment light, spectrum data of output printed matter, and the like, which will be described later.
The data input unit 11 stores the acquired reference spectrum data in the document data storage unit 12.

The image printed on the paper may be either a color image or a monochrome image, but here it is a color image.
In the present embodiment, the acquisition of the reference spectrum data is performed by using the image reading device. However, the present invention is not limited to using the image reading device. For example, the reference spectrum data has already been clarified. In some cases, the user can manually input it, or read the reference spectrum data stored in the recording medium, and the data input unit 11 can acquire the reference spectrum data.

The spectral correction processing unit 13 extracts the reference spectrum data from the document data storage unit 12, and uses the extracted reference spectrum data, the data relating to the spectrum of light reflected by the observation target surface P under the current light source (under the current light source). Spectrum data) (that is, the reference spectrum data is converted into spectrum data under the current light source), and the calculated spectrum data under the current light source is stored in the document data storage unit 12 (first light source spectrum calculation step, Step 11).
In this process, for example, when the user takes out the observation object O from the image reading apparatus and the image printed on the observation target surface P is viewed under the current light source, the spectrum of the light reflected by the observation target surface P Is calculated.
Under the current light source is, for example, a space where the observation object O exists when the user views an image printed on the observation object O taken out from the image reading apparatus. Specifically, it corresponds to the room a described in [Background Art].

Calculation of the spectrum data under the current light source based on the reference spectrum data can be performed by the following procedure.
For example, when the spectrum of the light reflected by the observation target surface P (reference spectrum data) is divided by the spectrum of the reference light source, the spectral reflectance of the observation target surface P is calculated. By multiplying the spectral reflectance of the observation target surface P by the spectrum of the current light source, the spectrum of light reflected by the observation target surface P under the current light source (spectrum data under the current light source) can be calculated (FIG. 15). (See (i) to (iii)).
This process is performed for each of all the pixels on the observation target plane P.
The current light source corresponds to a “first light source”. Further, the observation object O under the current light source corresponds to a “first object”. Furthermore, the spectrum of the light reflected by the observation target plane P of the observation object O under the current light source corresponds to the “first light source spectrum”.

The spectrum shape information calculation unit 14 extracts the spectrum data under the current light source from the document data storage unit 12, decomposes the extracted spectrum data under the light source for each intensity peak (maximum), and for each of the decomposed data, Spectrum shape information (“peak wavelength”, “half width”, “symmetry”, “resolved waveform intensity”) is calculated.
Specifically, the spectrum shape information calculation unit 14 specifies the maximum intensity of the spectrum data under the current light source, and calculates the intensity value at this maximum. For example, when the spectrum data under the current light source is represented by a waveform W0 as shown in FIG. 8, the intensity ts1 at the maximum s1 and the intensity ts2 at the maximum s2 are maximum values.

Next, the spectrum shape information calculation unit 14 calculates, for each local maximum, a waveform having an upwardly convex shape in which the local maximum value is the maximum value, and uses this waveform as a decomposition waveform. For example, the waveform W0 of the spectrum data under the current light source shown in FIG. 8 is divided into decomposition waveforms W1 and W2.
These decomposition waveforms W1 and W2 have a shape including a portion including one maximum in the waveform W0 of the spectrum data under the current light source in principle. However, within a predetermined range in wavelength (for example, 380 nm to 780 nm), the spectrum data under the current light source is cut off halfway and does not reach the baseline (described later), or between one maximum and the adjacent maximum In this part, spectrum shape information required for cluster analysis (described later) cannot be calculated. Therefore, in these portions, the spectrum data under the current light source is differentiated to obtain a tangent, and a decomposition waveform is calculated (formed) while including a part of the tangent.
By calculating the decomposition waveform in this way, the waveform indicating the spectrum data under the current light source can be decomposed into a plurality of decomposition waveforms.
The baseline is a waveform of a linear function indicating one value (ta) in intensity. This baseline is also a line representing the minimum value (ta) of the intensity indicated by the decomposition waveform. Each decomposed waveform preferably includes at least wavelength data at the intensity ta.

Subsequently, the spectrum shape information calculation unit 14 calculates spectrum shape information (“peak wavelength”, “half width”, “symmetry”, “resolved waveform intensity”) for each decomposed waveform.
Here, the “peak wavelength” refers to the value of the wavelength when the intensity shows the maximum value (maximum value) in the decomposed waveform.
“Half width” refers to the difference between the two wavelengths (full width at half maximum) at the intensity tb in the decomposition waveform. Here, the intensity tb is an intensity obtained by adding a value obtained by dividing the difference between the maximum value (for example, ts1) of the decomposed waveform intensity and the baseline intensity (ta) by 2 to the baseline intensity (ta). Say.
“Symmetry” refers to the difference between the wavelength at the right side and the wavelength at the left side of the “half width” obtained by dividing the “half width (full width at half maximum)” from the peak wavelength.
The “decomposed waveform intensity” refers to the intensity of the spectrum indicated by the decomposed waveform.
By calculating the shape information of these spectra for each decomposition waveform, the spectrum data under the current light source can be decomposed into spectrum data (decomposition spectrum data) indicating the decomposition waveform.
The spectrum shape information calculation unit 14 calculates the spectrum shape information for each pixel and stores it in the document data storage unit 12 (shape information acquisition process, step 12). The spectrum shape information calculated for each pixel is shown in FIG.

The cluster analysis calculation unit 15 extracts the spectrum shape information of the current light source on the observation target surface P from the document data storage unit 12, performs cluster analysis on the extracted spectrum shape information, and performs cluster analysis on the cluster analysis result. The result is stored in the result storage unit 16 (cluster analysis process, step 13).
Further, the cluster analysis calculation unit 15 takes out the result of the cluster analysis from the cluster analysis result storage unit 16, and based on the result of this cluster analysis, the positions in the observation target plane P are adjacent and the spectrum shape information is similar. Are formed (cluster formation process) and stored in the cluster analysis result storage unit 16 (step 14).
Further, the cluster analysis calculation unit 15 extracts the cluster analysis result from the cluster analysis result storage unit 16, acquires the similarity of the spectrum shape information between the clusters based on the result of the cluster analysis, and stores the cluster analysis result. Stored in the unit 16 (similarity acquisition processing, step 15).

The processing of step 13 to step 15 will be further described.
There are various methods for cluster analysis. For example, there are hierarchical methods such as the shortest distance method, longest distance method, group average method, and Ward method, and division optimization methods such as the k-means method. Which method is used can be arbitrarily determined, but in this embodiment, a hierarchical method is used.
The cluster analysis is executed in, for example, step 69 and step 74 in FIG. 6 and step 20 in FIG. 3 in addition to steps 13 to 15 in FIG. The cluster analysis in Step 69, Step 74, and Step 20 differs only in the data to be handled, and the basic procedure is the same as the cluster analysis procedure in Step 13 to Step 15.
Here, the general procedure of the hierarchical method will be described first, and then, an example of cluster analysis of the spectrum data under the current light source by the hierarchical method (the processing procedure of step 13 to step 15) will be described.

The hierarchical method, which is one method of cluster analysis, is performed in the following procedure.
(Procedure a) A matrix of data is created using given data as an element.
(Procedure b) A matrix of distance (or similarity) is calculated from the matrix of data. Examples of this calculation method include a method of calculating the Euclidean distance and a method of calculating the Euclidean square distance.
(Procedure c) A cofen matrix is calculated from a matrix of distance (or similarity). For example, when the distance matrix is composed of N data, an initial state with N clusters including only one data is created. Starting from this state, the distance d (C1, C2) between the clusters is calculated from the distance d (x1, x2) (dissimilarity) between the data x1 and x2, and the two nearest clusters are sequentially calculated. To merge. Then, this merging is repeated until all data is merged into one cluster, thereby obtaining a hierarchical structure. In this (procedure c), the matrix after the initial state (the matrix after the first merging of the two clusters) is the Cofen matrix.

(Procedure d) A dendrogram (dendrogram) is created based on the cofen matrix. As shown in FIG. 10, the dendrogram is a binary tree in which each terminal node represents each object and a cluster formed by merging is represented by a non-terminal node.
(Procedure e) Set scale criteria and determine clusters.
(Procedure f) The similarity is specified for each cluster.

Among these (procedure a) to (procedure f), (procedure a) to (procedure c) (arithmetic processing relating to a matrix) corresponds to step 13 of this embodiment, and (procedure c) to (procedure e) (cluster) (Computation processing related to merging and determination) corresponds to step 14, and (procedure f) corresponds to step 15.
When performing cluster analysis of spectrum data under the current light source using the hierarchical method, it can be performed by the following procedure.
When the cluster analysis calculation unit 15 extracts the spectrum shape information (see FIG. 9) from the document data storage unit 12, the cluster analysis calculation unit 15 creates a matrix using the spectrum shape information as an element (procedure a). Here, as described above, the spectrum shape information includes “peak wavelength”, “half-value width”, “symmetry”, and “decomposed waveform intensity”. create.

A distance matrix is calculated from a matrix (data matrix) having the shape information of the spectrum as an element, for example, using the Euclidean distance (procedure b). Then, each element constituting the calculated distance matrix is defined as a cluster.
Next, two clusters having the shortest distance are selected from the calculated distance matrix, and the two selected clusters are merged into one cluster. Then, a new Cofen matrix is calculated from the Cofen matrix, which is the matrix after merging, using, for example, the Euclidean distance (procedure c).
Subsequently, the two clusters having the closest distance are selected from the calculated cofen matrix, and the two selected clusters are merged into one cluster. Then, a new Cofen matrix is calculated from the Cofen matrix, which is the matrix after merging, using, for example, the Euclidean distance (procedure c).
The merging of these clusters and the calculation of the Cofen matrix are repeated until all clusters are merged into one cluster.

Once all clusters have been merged into one cluster, this time the scale criteria setpoint is established (procedure d). Then, a cluster merged at a distance shorter than the scale reference set value is selected (procedure e). The selected cluster is the cluster obtained in step 15, and the distance at which these clusters are merged is set as the similarity of the cluster (procedure f).
In addition, if cluster analysis is performed for each spectrum shape information, the pixels grouped as a cluster differ from element to element, but similarity from elements with high priority (peak wavelength, resolution waveform intensity, half-value width, ...) The one with the highest is made into a cluster in order. Eventually, similar high pixels of all elements are clustered. In order to compare the relationship of the similarity of each element so that there is no change before and after processing, the similarity of each element is examined.
The cluster analysis calculation unit 15 acquires the similarity of the spectrum shape information between clusters from the result of the cluster analysis, and stores it in the cluster analysis result storage unit 16.

Then, “observation environment light correction calculation” is executed (step 16).
This “observation environment light correction calculation” will be described in detail later in “(3) Observation environment light correction calculation process”.

Subsequently, “output paper correction processing” is executed (step 17).
The “output paper correction process” will be described in detail in “(5) Output paper correction process” described later.

The spectral correction processing unit 13 converts the corrected spectrum F ′ into a spectrum C ′ under the observation environment light (step 18).
Specifically, the spectrum F ′ after correction is divided by the spectrum A of the current light source to calculate the spectral reflectance E ′ of the output printed matter (second spectral reflectance calculation step, FIG. xii)).
Next, a spectrum C ′ under the observation environment light is calculated by multiplying the spectrum D of the observation environment light by the spectral reflectance E ′ of the output printed matter ((xiii) to (xv) in the figure).
The spectral reflectance E ′ of the output printed matter corresponds to “second spectral reflectance”. The “second spectral reflectance calculation step” includes “output paper correction processing” (described later).

The spectrum shape information calculation unit 14 calculates light spectrum shape information (step 19). Here, the waveform of the spectrum is decomposed for each peak based on the intensity at each wavelength. Then, shape information (“peak wavelength”, “half width”, “symmetry”, “decomposed waveform intensity”) is calculated for each waveform of the decomposed spectrum.
The cluster analysis calculation unit 15 performs cluster analysis based on the corrected spectrum shape information and coordinates under the designated light source of the observation target surface P, and stores the result in the cluster analysis result storage unit 16 (step 20). ).

The computing unit 20 retrieves the cluster analysis result from the cluster analysis result storage unit 16 and compares the spectrum similarity ratio under the current light source with the corrected spectrum similarity (step 21).
Based on the cluster analysis result of the cluster analysis result storage unit 16, the calculation unit 20 corrects the similarity of the spectrum shape of the observation target surface P so as to approach the relationship under the original light source (step 22). . At that time, the relationship between objects having a short distance on the observation surface is corrected with priority.

(3) Observation Environment Light Correction Calculation Processing This processing reaches, for example, the observation target surface P when the user views an image printed on the observation target surface P of the observation object O under the observation environment light. The spectrum of light is calculated, and the spectrum of light reflected on the observation environment plane P is corrected using the calculated spectrum of light.

As shown in FIG. 4, the color correction processing apparatus 1 designates an observation environment (step 30). Specifically, the user inputs data relating to the observation object O and the obstacle as environment data by operating the panel 17 constituted by a touch panel or the like. As a result, the panel 17 receives a user designation of environmental data.
Here, the obstacle refers to an object that is present around the observation object O in the observation environment, and the light reflected or transmitted through the obstacle reaches the observation object O. Specifically, for example, walls and windows are included.

The environmental data includes data relating to the arrangement and the like of the observation object O and data relating to the type and arrangement of the obstacle.
The data relating to the arrangement or the like of the observation object O includes, for example, coordinates indicating the position where the observation object O is arranged in the observation environment, coordinates indicating the size of the observation object O, and the like. Note that the observation object O in the observation environment corresponds to a “second object”.
The data regarding the type and arrangement of the obstacle includes, for example, the name of the obstacle, the spectral reflectance of the obstacle, the light transmittance of the obstacle, the coordinates indicating the position where the obstacle is arranged, and the size of the obstacle. The coordinates shown are included.
The panel 17 stores the input environmental data in the environmental data storage unit 18.

Further, the color correction processing device 1 designates the type and position of the observation environment light (step 31). Specifically, the user inputs data regarding the type and position of the light source S of the observation environment light L as the environment light data by operating the panel 17 configured by a touch panel or the like. As a result, the panel 17 receives user designation of ambient light data.
Here, the ambient light data includes data related to the type and arrangement of the light source S of the observation environment light L, specifically, for example, the type of the light source S that emits the observation environment light L, the spectrum of the observation environment light L, the observation The coordinates indicating the position where the light source S is arranged in the environment are included.
The light source S arranged in the observation environment corresponds to a “second light source”.
The panel 17 stores the input ambient light data in the environment data storage unit 18.

  The computing unit 20 extracts the environmental data (data regarding the observation object O) and the environmental light data from the environmental data storage unit 18, and based on the environmental data and the environmental light data, the light beams from the observation environmental lights L1 and L2 are extracted. The inner product of the vector L → and the normal vector n → of the observation target surface P is calculated (step 32).

Specifically, the arithmetic unit 20 extracts data relating to the observation object O and ambient light data from the environment data storage unit 18.
Next, the position of each pixel on the observation target plane P is specified based on the data related to the observation target O, and the normal vector n → of the observation target plane P is calculated for each pixel. Note that the length of the normal vector n → is proportional to the spectral reflectance in each pixel.
Subsequently, based on the position of each pixel on the observation target plane P and the positions of the light sources S1 and S2 of the observation environment light L1 and L2, the calculation unit 20 calculates a vector L of light rays from the light sources S1 and S2 toward the pixels. → is calculated.

Further, the calculation unit 20 calculates an angle (angle formed) between the light vector L → and the normal vector n → for each pixel.
Then, the arithmetic unit 20 uses, for each pixel, the inner product L → · n → of the light vector L → and the normal vector n → using the angle formed by the light vector L → and the normal vector n →. Calculate

The cluster analysis calculation unit 15 extracts the environmental light data from the environmental data storage unit 18 and receives the inner product L → · n → from the calculation unit 20, and the starting point of the light vector L → obtained from the environmental light data and the inner product L Cluster analysis is performed for .fwdarw..multidot..fwdarw. (That is, cluster analysis is performed for inner products L.fwdarw..multidot.n.fwdarw. For each of the light sources S1 and S2) to form a cluster of highly similar vectors (step 33).
Although the cluster analysis is as described above, the matrix of data created first is created for each light source S1, S2 with the inner product L → · n → as an element.

When the calculation unit 20 receives the result of the cluster analysis for the starting point of the ray vector L → and the inner product L → · n → from the cluster analysis calculation unit 15, the calculation unit 20 receives the rays included in the cluster formed in the cluster analysis calculation unit 15. Is synthesized.
For example, as shown in FIG. 11, when the light beam L1 and the light beam L2 are included in the same cluster, the vector L1 → of the light beam L1 and the vector L2 → of the light beam L2 are combined to generate a vector L of the light beam L ′. '→ get.

  Then, the calculation unit 20 calculates the inner product L ′ → · n → of the light vector L ′ → and the normal vector n → of the observation target surface P, and the inner product L ′ → · n → is (L ′ → .n →) It is determined whether or not <0 (step 34). As a result of the determination, when the inner product L ′ → · n → (L ′ → · n →) <0, “reflected light / transmitted light calculation” is performed (step 35). This “reflected light / transmitted light calculation” will be described in detail later in “(4) Reflected light / transmitted light calculation process”.

On the other hand, when the inner product L ′ → · n → is not (L ′ → · n →) <0, based on the environmental data and the environmental light data extracted from the environmental data storage unit 18, each observation environmental light L1, It is determined whether there is a surrounding obstacle on the extended line of the light beam from L2 (step 35).
If there is an obstacle around it as a result of the determination, "reflected light / transmitted light calculation" is performed (step 36).
On the other hand, when there is no surrounding obstacle, the process after step 37 described below is executed. Even when the “reflected light / transmitted light calculation” process is completed, the processes after step 37 described below are executed.

The computing unit 20 takes out environmental data from the environmental data storage unit 18 and observes from the distance rO from the light sources S1 and S2 of the observation environment lights L1 and L2 to the observation object O, or from the final reflection surface or the final transmission surface. A distance rO to the object O is calculated (step 37).
Here, the former distance rO is a distance calculated when there is no obstacle between the light sources S1 and S2 and the observation object O. On the other hand, the latter distance rO is a distance calculated when there is an obstacle between the light sources S1 and S2 and the observation object O. That is, the distance between the intersection point B and the observation target surface P shown in FIG. 3 is calculated as the distance rO.
The calculation unit 20 stores the calculated distance rO in the environment data storage unit 18.

The spectral correction processing unit 13 extracts the light spectrum of the observation environmental light from the environmental light spectral data storage unit 19 and extracts the distance rO from the light sources S1 and S2 to the observation object O from the environmental data storage unit 18. However, when the process of “reflected light / transmitted light calculation” is executed, the spectral correction processing unit 13 extracts the spectrum of the LR or LT rays from the spectral data storage unit 21 and the final data from the environment data storage unit 18. A distance rO from the reflection surface or the final transmission surface to the observation object O is taken out.
Then, the spectral correction processing unit 13 calculates the spectrum LrO by dividing the intensity of the spectrum of the light L of the observation environment light (or the spectrum of LR or LT) by the square of the distance rO for each wavelength, The spectral data is stored in the spectral data storage unit 21 (step 38).
This is because the intensity of the spectrum of the light source is attenuated in proportion to 1 / square of the distance, as shown in FIG.

  The spectral correction processing unit 13 takes out the light spectrum LrO from the spectral data storage unit 21 and adds (synthesizes) the intensities of all the light spectra LrO reaching the observation object O for each wavelength (that is, the light source). The spectrum of the light beam from S and the spectrum of the reflected or transmitted light from the surrounding obstacle are synthesized for each pixel, and the spectrum of the added (synthesized) light beam is the spectrum D of the light beam that reaches the observation object O. Is stored in the spectral data storage unit 21 (step 39).

Then, the “ambient light correction process” is executed (step 40). The “ambient light correction process” is executed in the following procedure.
As shown in FIG. 5, the spectral correction processing unit 13 extracts from the document data storage unit 12 the spectrum of light reflected by the observation target surface P under the current light source (current light source spectrum data C) (step 50). ).
The spectral correction processing unit 13 calculates the spectral reflectance (spectrum E) of the observation target surface P based on the extracted spectrum data C under the current light source and the spectrum D of the light beam that has reached the observation target O, and the spectral The data is stored in the data storage unit 21 (first spectral reflectance calculation step, step 51).
The spectrum E can be calculated using the following formula (see FIGS. 15 (iii) to (vi)).
Spectrum E = Spectrum C ÷ Spectrum D (Formula 1)
The spectrum E calculated using this equation 1 is the spectral reflectance (E) of the output printed matter necessary for color reproduction.

Note that “Spectrum C” in Expression 1 substitutes the spectrum data C under the current light source, and this is the observation target surface of the observation object O under the current light source spectrum data C and the observation environment light. This is for calculating the spectral reflectance (E: first spectral reflectance) of the observation target surface P when the spectrum data of the light reflected by P is the same.
The spectrum of the light reflected by the observation target surface P of the observation object O under the observation environment light corresponds to the “second light source spectrum”.

The spectral correction processing unit 13 calculates the spectral distribution (spectrum F) of the light reflected from the output printed matter with the current light source, and stores it in the spectral data storage unit 21 (step 52).
The spectrum F can be calculated using the following equation (see FIGS. 15 (vii) to (ix)).
Spectrum F = Spectrum A × Spectrum E (Formula 2)
In Equation 2, the spectrum A is the current light source spectrum.

  Then, the “environment light correction process” is ended, and the “observation environment light correction calculation process” is ended.

(4) Reflected / Transmitted Light Calculation Process The “reflected / transmitted light calculation process” shown in step 36 of FIG. 4 is executed in the following procedure.
As illustrated in FIG. 6, when the calculation unit 20 and the spectral correction processing unit 13 take out the environmental data and the environmental light data (data regarding the light sources S1 and S2) from the environmental data storage unit 18, each of the observation environmental lights L1 and L2 is obtained. It is determined whether or not a surrounding obstacle exists on the extended line of the light beam (step 60).
As a result of the determination, if there is no surrounding obstacle, the spectral correction processing unit 13 sets the spectrum of the light of the observation environment light that does not reach the observation object O to 0 and stores it in the spectral data storage unit 21 (step). 61). Then, the “reflected light / transmitted light calculation process” is terminated, and the processes after step 37 shown in FIG. 4 are executed.

On the other hand, when the surrounding obstacle exists, the calculation unit 20 determines the light of the observation environment light L1 and L2, the surrounding obstacle surface, based on the environment data and the environment light data extracted from the environment data storage unit 18. The coordinates of the intersection point B are calculated (step 62).
Based on the environmental data and the environmental light data, the arithmetic unit 20 determines the light sources S1 and S2 (or reflection points and transmission points of other obstacles) of the observation environmental light L1 and L2 and the intersection B between the surrounding obstacle surfaces. The distance rB is calculated (step 63).
Based on the environmental data and the environmental light data, the arithmetic unit 20 calculates the angle θ formed by the vector L → of the light beams of the observation environmental lights L1 and L2 and the surrounding obstacle surface (step 64).
The spectral correction processing unit 13 extracts the environmental light data from the environmental data storage unit 18, and calculates the spectrum LrB obtained by dividing the intensity of the spectrum L of the light of the observation environmental light L1 and L2 by the square of the distance rB for each wavelength. And stored in the spectral data storage unit 21 (step 65).

Thereafter, the processing from step 66 to step 70 and the processing from step 71 to step 75 are executed in parallel.
The spectral correction processing unit 13 extracts the spectrum of the observation environment light after the distance correction from the spectral data storage unit 21 and also extracts environmental data (data on the obstacle) from the environmental data storage unit 18. Then, the spectral correction processing unit 13 calculates the spectrum LR by multiplying the spectrum LrB of the light of the observation environment light by the reflectance spectrum of the obstacle, and stores it in the spectral data storage unit 21 (step 66). The spectrum LR corresponds to a “final reflection / transmission spectrum”.
This spectrum LR is a spectrum of light reflected by the obstacle, and is obtained by multiplying the spectrum L of the light source and the reflectance spectrum R of the obstacle as shown in FIG.

Based on the environmental data from the environmental data storage unit 18, the arithmetic unit 20 starts from the intersection point B, and the vector LR → (reflected light) obtained by rotating the light vector of the observation environmental light by 2θ to the incident side with respect to the obstacle surface Vector) is calculated (see FIG. 2 for step 67, “2θ”).
The computing unit 20 calculates the inner product of the reflected light vector LR → of the light beam and the normal vector n → of the observation target surface P (step 68).

The cluster analysis calculation unit 15 performs a cluster analysis on the start point of the ray vector LR → obtained from the environment data of the environment data storage unit 18 and the inner product LR → · n → calculated by the calculation unit 20, and a vector having high similarity Are formed (step 69).
The calculation unit 20 synthesizes the rays included in the cluster formed in the cluster analysis calculation unit 15 and calculates the inner product LR ′ → · n → with the normal vector n → of the observation target surface P as the ray vector LR ′ →. Then, it is determined whether or not the inner product LR'.fwdarw.n.fwdarw. (LR'.fwdarw.n.fwdarw.) <0 (step 70).
As a result of the determination, when the inner product is (LR ′ → · n →) <0, the reflected light / transmitted light calculation is terminated, and the processing from step 37 shown in FIG. 4 is executed.
On the other hand, when the inner product is not (LR ′ → · n →) <0, the processing from step 60 shown in FIG. 6 is executed.

When the process of step 65 is executed, the processes of steps 71 to 75 described below are executed in parallel with the processes of steps 66 to 70 described above.
The spectral correction processing unit 13 extracts the spectrum of the observation environmental light beam after the distance correction from the spectral data storage unit 21 and the environmental light data (data on the obstacle) from the environmental data storage unit 18. Then, the spectral correction processing unit 13 calculates the spectrum LT by multiplying the light spectrum LrB of the observation environment light by the transmittance spectrum of the obstacle, and stores the spectrum LT in the spectral data storage unit 21 (step 71). The spectrum LT corresponds to a “final reflection / transmission spectrum”.
The spectrum LT is a spectrum of light transmitted by the obstacle, and is obtained by multiplying the spectrum L of the light source and the transmittance spectrum T of the obstacle as shown in FIG.

The computing unit 20 calculates a vector LT → (transmitted light vector) starting from the intersection B based on the environmental data from the environmental data storage unit 18 (step 72).
The computing unit 20 calculates the inner product of the transmitted light vector LT → and the normal vector n → of the observation target surface P (step 73).
The cluster analysis calculation unit 15 performs cluster analysis on the starting point of the light vector LT → obtained from the environment data of the environment data storage unit 18 and the inner product LT → · n → calculated by the calculation unit 20, and a vector having high similarity Are formed (step 74).

The calculation unit 20 synthesizes the rays included in the cluster formed in the cluster analysis calculation unit 15 and calculates the inner product LT ′ → · n → with the normal vector n → of the observation target plane P as the ray vector LT ′ →. Then, it is determined whether or not the inner product LT ′ → · n → is (LT ′ → · n →) <0 (step 75).
As a result of the determination, when the inner product is (LT ′ → · n →) <0, the calculation of the reflected light / transmitted light is terminated, and the processing after step 37 shown in FIG. 4 is executed.
On the other hand, when the inner product is not (LT ′ → · n →) <0, the processing after step 60 shown in FIG. 6 is executed.

(5) Output Paper Correction Process As shown in FIG. 7, the data input unit 11 acquires the spectrum data of the background color of the output paper under a reference light source (for example, CIE standard light source D50), and uses this as the background color. The spectrum data is stored in the document data storage unit 12 (background color acquisition process, step 80).
The “output paper” here is a paper on which the image forming apparatus performs a printing process after the color correction processing apparatus and the image forming apparatus perform color correction by executing the [image processing method]. Say.
Further, the process of step 80 can be performed with the same contents as the process of step 10 shown in FIG. That is, an image reading device such as a scanner reads one surface of the output paper and calculates the background color spectrum data based on the data obtained by the reading (or the spectroscope provided in the image reading device The spectrum of the light reflected by the output paper is detected to calculate the background color spectrum data), and the data input unit 11 can acquire the reference spectrum data.

The spectral correction processing unit 13 determines whether or not (spectral intensity of the spectrum F) ≧ (spectral intensity of the background color spectrum α under the current light source) (step 81).
As a result of the determination, if (spectral intensity of spectrum F) ≧ (spectral intensity of background color spectrum α under the current light source), the spectral correction processing unit 13 stores the spectrum F in the spectral data storage unit 21 ( Step 82).
Then, the “output sheet correction process” is ended, and the processes after step 18 in FIG. 3 are executed.

On the other hand, when (spectral intensity of spectrum F) ≧ (spectral intensity of background color spectrum α under the current light source) is not satisfied, the spectral correction processing unit 13 determines the spectral intensity MAX value and spectrum F spectrum of the background color spectrum α. All spectral intensities are corrected at the same ratio so that the MAX values of the intensities match (step 83).
Next, the spectral correction processing unit 13 stores the corrected spectrum F ′ in the spectral data storage unit 21 (step 84).
Then, the “output sheet correction process” is ended, and the processes after step 18 in FIG. 3 are executed.

When all the processes shown in the flowcharts of FIGS. 3 to 7 are completed, the following process is performed in the image forming apparatus equipped with the color correction processing apparatus.
The corrected spectrum is converted to XYZ, and the XYZ value is converted to Lab. The image forming apparatus has a look-up table for converting from Lab to RGB or from Lab to toner CMYK values according to characteristics, and determines an output value using this look-up table.

[Image processing program]
Next, the image processing program will be described.
The image processing function (function for executing the image processing method) of the computer (image forming apparatus, color correction processing apparatus) in each of the above embodiments is stored in a storage unit (for example, a ROM (Read Only Memory) or a hard disk). This is realized by a stored image processing program.

The image processing program is read by a computer control means (CPU (Central Processing Unit) or the like) to send instructions to each component of the computer to perform predetermined processing, for example, color correction processing of the color correction processing device, observation environment Light correction calculation processing, reflected / transmitted light calculation processing, output paper correction processing, and the like are performed.
As a result, the image processing function is realized by the cooperation of the image processing program as software and each component of the computer (image forming apparatus, color correction processing apparatus) as hardware resources.

Note that an image processing program for realizing the image processing function is stored in a computer ROM, hard disk, or the like, or may be stored in a computer-readable recording medium such as an external storage device and a portable recording medium. it can.
An external storage device is a CD-ROM (Compact Disk-Read
A memory expansion device that has a built-in storage medium such as “Only Memory” and is externally connected to the image forming apparatus. On the other hand, the portable recording medium is a recording medium that can be mounted on a recording medium driving device (drive device) and is portable, and refers to, for example, a flexible disk, a memory card, a magneto-optical disk, and the like.

The program recorded on the recording medium is stored in a computer RAM (Random Access).
It is loaded into a memory and the like and executed by a CPU (control means). With this execution, the function of the image forming apparatus of the above-described embodiment is realized.
Further, when the image processing program is loaded by the computer, the image processing program held by another computer can be downloaded to its own RAM or external storage device using a communication line. The downloaded image processing program is also executed by the CPU, and realizes the image processing function of the image forming apparatus of the embodiment.

  As described above, according to the image processing method and the image processing program of the present embodiment, not only the spectrum of the light source but also the spectrum of light reflected or transmitted by another object and the color of the paper (background color) are taken into consideration. Therefore, color correction can be performed accurately. Therefore, it is possible to perform color reproduction that does not change the impression of color arrangement when printed on different papers and observed under a plurality of types of ambient light that exists at various positions.

Further, by using the similarity of cluster analysis for the spectrum of the observation object (printed material), correction can be made so as to maintain the balance of the color arrangement.
Furthermore, the cluster of the spectrum of the light emitted from the light source to the printed matter and the spectrum of the light that is reflected or transmitted through the obstacle and applied to the printed matter reduces the amount of calculation of the spectrum synthesis. it can.

The preferred embodiments of the image processing method and the image processing program of the present invention have been described above. However, the image processing method and the image processing program according to the present invention are not limited to the above-described embodiments, and the scope of the present invention. Needless to say, various modifications can be made.
For example, in the above-described embodiment, the color correction processing apparatus that is assumed to be mounted on the image forming apparatus has been described. However, the color correction processing apparatus is not limited to be mounted on the image forming apparatus, and performs color correction. It can be mounted on various devices.

  Since the present invention is an invention related to color correction, it can be used in an apparatus or apparatus that performs color correction.

DESCRIPTION OF SYMBOLS 1 Color correction processing apparatus 11 Data input part 12 Original data storage part 13 Spectral correction processing part 14 Spectral shape information calculation part 15 Cluster analysis calculation part 16 Cluster analysis result storage part 17 Panel 18 Environmental data storage part 19 Ambient light spectral data storage part 20 Calculation unit 21 Spectral data storage unit

Claims (8)

A first light source under spectrum calculation step of calculating a spectrum of light reflected by the target surface of the first object under the first light source as a first light source spectrum;
When the spectrum of light reflected from the target surface of the second object under the second light source is the second light source spectrum, the second light source spectrum is the same as the first light source spectrum. A first spectral reflectance calculation step of calculating the spectral reflectance of the target surface of the second object as the first spectral reflectance;
Using the data calculated based on the first spectral reflectance, and a color correction step of performing color correction of an image printed on a printed matter,
The spectrum calculation step under the first light source includes
Processing for calculating the spectrum under the first light source for each of a plurality of pixels on the target surface of the first object;
Performing a cluster analysis on the calculated spectrum under the first light source for each of a plurality of pixels to form a plurality of clusters,
The first spectral reflectance calculation step includes
The spectrum of light reaching the target surface of the second object is cluster-analyzed to form a plurality of clusters, and the cluster-analyzed spectrum and the cluster-analyzed spectrum under the first light source are used. The image processing method characterized by including the process which calculates one spectral reflectance. The spectrum calculation step under the first light source includes
Shape information acquisition processing for acquiring shape information of the spectrum under the first light source for each of the plurality of pixels;
Based on the acquired shape information, an analysis process for performing cluster analysis on the spectrum under the first light source,
Based on the result of this cluster analysis, a cluster forming process for forming a plurality of clusters having similar shape information,
The image processing method according to claim 1, further comprising: similarity acquisition processing for acquiring similarity of spectrum shape information between clusters. The first spectral reflectance calculation step includes
A process of calculating an inner product of a vector of rays directly reaching the target surface of the second object from the second light source and a normal vector from the target surface of the second object;
A cluster analysis is performed on the starting point of the ray vector and the inner product to form a plurality of clusters;
A process of calculating a final reflection / transmission spectrum of the light reflected or transmitted through a predetermined obstacle and reaching the target surface of the second object;
A cluster analysis is performed on the final reflection / transmission spectrum to form a plurality of clusters,
A process of combining the spectrum of light directly reaching the target surface of the second object from the second light source and the final reflection / transmission spectrum based on the formed clusters,
The image processing method according to claim 1, further comprising a process of calculating the first spectral reflectance using a spectrum obtained by the synthesis and the spectrum under the first light source. The spectrum under the first light source before correction of the spectrum of the light reflected by the target surface of the first object under the first light source when the spectral reflectance of the first object is the first spectral reflectance. The corrected spectrum under the first light source before correction is corrected based on the background color of the first object, and the corrected spectrum under the first light source and the spectrum of the first light source are corrected. A second spectral reflectance calculation step of calculating the spectral reflectance of the target surface of the first object as the second spectral reflectance,
The color correction step performs color correction of an image to be printed on the printed material based on the second spectral reflectance calculated in the second spectral reflectance calculation step. An image processing method according to claim 1. The second spectral reflectance calculation step includes
A background color acquisition process for acquiring, as a background color spectrum, a spectrum of light reflected from the target surface when the target surface of the first object is a background color;
When the intensity of the spectrum under the first light source is larger than the intensity of the background color spectrum, the maximum value of the spectral intensity of the background color spectrum and the maximum value of the spectrum intensity of the spectrum under the first light source match. Processing for correcting the spectrum under the first light source based on the background color of the first object by correcting all the spectral intensities at the same ratio and storing the corrected background color spectrum. The image processing method according to claim 4. Converting the spectrum under the first light source after correction based on the base color into the spectrum under the second light source based on the second spectral reflectance;
Calculating the shape information of the spectrum under the converted second light source;
Performing cluster analysis based on the shape information and coordinates of the spectrum after correction under the designated light source of the target surface of the second object;
Based on the result of this cluster analysis, comparing the ratio of the similarity of the spectrum under the second light source and the similarity of the spectrum after correction,
A step of correcting the similarity of the shape of the spectrum of the target surface of the second object so as to approach the relationship under the first light source based on the result of the cluster analysis. The image processing method according to claim 4 or 5. A reference spectrum acquisition step of acquiring, as a reference spectrum, a spectrum of light reflected by the target surface of the first object under a reference light source;
The image processing method according to claim 1, wherein the spectrum calculation step under the first light source includes a process of calculating the spectrum under the first light source based on the reference spectrum. A first light source under spectrum calculation step of calculating a spectrum of light reflected by the target surface of the first object under the first light source as a first light source spectrum;
When the spectrum of light reflected from the target surface of the second object under the second light source is the second light source spectrum, the second light source spectrum is the same as the first light source spectrum. A first spectral reflectance calculation step of calculating the spectral reflectance of the target surface of the second object as the first spectral reflectance;
Using the data calculated based on the first spectral reflectance, and causing the color correction processing device to execute a color correction step of performing color correction of an image printed on a printed matter,
In the first light source spectrum calculation step,
Processing for calculating the spectrum under the first light source for each of a plurality of pixels on the target surface of the first object;
Performing a cluster analysis on the calculated spectrum under the first light source for each of a plurality of pixels, and causing the color correction processing device to execute a process of forming a plurality of clusters,
In the first spectral reflectance calculation step,
The spectrum of light reaching the target surface of the second object is cluster-analyzed to form a plurality of clusters, and the cluster-analyzed spectrum and the cluster-analyzed spectrum under the first light source are used. An image processing program that causes the color correction processing device to execute a process of calculating one spectral reflectance.

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