CN110726536B - Color correction method for color digital reflection microscope - Google Patents

Color correction method for color digital reflection microscope Download PDF

Info

Publication number
CN110726536B
CN110726536B CN201910907891.1A CN201910907891A CN110726536B CN 110726536 B CN110726536 B CN 110726536B CN 201910907891 A CN201910907891 A CN 201910907891A CN 110726536 B CN110726536 B CN 110726536B
Authority
CN
China
Prior art keywords
color
values
rgb
matrix
reflection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910907891.1A
Other languages
Chinese (zh)
Other versions
CN110726536A (en
Inventor
毛磊
徐鹏
张克奇
张琦
邱元芳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NINGBO YONGXIN OPTICS CO Ltd
Original Assignee
NINGBO YONGXIN OPTICS CO Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NINGBO YONGXIN OPTICS CO Ltd filed Critical NINGBO YONGXIN OPTICS CO Ltd
Priority to CN201910907891.1A priority Critical patent/CN110726536B/en
Publication of CN110726536A publication Critical patent/CN110726536A/en
Application granted granted Critical
Publication of CN110726536B publication Critical patent/CN110726536B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/04—Optical benches therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/0285—Testing optical properties by measuring material or chromatic transmission properties
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands

Landscapes

  • Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Image Processing (AREA)
  • Facsimile Image Signal Circuits (AREA)

Abstract

The invention discloses a color correction method of a color digital reflection microscope, which utilizes a large-size reflection color chip to correct the color of a microscopic image acquired by the color digital reflection microscope, and because an illumination light source of the digital microscope is usually fixed, the chromaticity characterization of a digital camera under the fixed condition is particularly suitable for the color correction of the digital microscope, the method utilizes a color block in the large-size reflection color chip as a training sample to establish a forward chromaticity characterization model of the color digital reflection microscope; the method can realize the accurate correction of the color of the shot target, improve the color accuracy of the color digital reflection microscope, accord with the color perception of human eyes, realize the effect of what you see is what you get, obviously improve the identification degree of the target object in the microscopic detection, and realize the accurate identification and positioning of the target object.

Description

Color correction method for color digital reflection microscope
Technical Field
The invention relates to a color correction method for a microscope, in particular to a color correction method for a color digital reflection microscope.
Background
In color digital imaging systems, there are increasing demands placed on the color quality of images. The colors of digital microscopic imaging systems are formed with the particularity that, on the one hand, they are adapted to the sensory properties of the human eye and, on the other hand, they reflect the primary colors of the object as realistically as possible. A large part of the existing color digital reflection microscopes are applied to medical detection, and a plurality of pathological characteristics are distinguished by the color change of cells, so that the requirement on color authenticity is high. The microscopic image processing software is an integral part of a digital microscopic imaging system, converts data acquired by an image sensor into a color image consistent with human vision, and provides powerful image post-processing and analysis functions. However, the processing software has a very high requirement on the color quality of the image of the digital microscopic imaging system, and if the color quality of the image is not good, the visual effect is affected, and the post-processing and analysis of the image are also greatly affected, so that the color correction of the image of the color digital reflective microscope is required. At present, most of color correction solutions adopt a white balance algorithm, however, the white balance algorithm is only a rough color correction method, and accurate color correction of a digital microscopic imaging system cannot be realized.
Disclosure of Invention
The invention aims to provide a color correction method of a color digital reflection microscope, which can improve the color quality of an image.
The technical scheme adopted by the invention for solving the technical problems is as follows: a color correction method for a color digital reflection microscope comprises the following steps:
(1) obtaining RGB values of all color blocks in a large-size reflection color card by using a color camera on a color digital reflection microscope, and defining the RGB values as an RGB matrix with the number of the color blocks on the reflection color card as a line number and the number of color camera channels as a column number, wherein each line represents 3-channel response values of RGB corresponding to one color block, and each column represents response values of all the color blocks output by one channel;
(2) the method comprises the steps of continuously shooting after an illumination light source of a microscope is turned off, obtaining dark current response values of a color camera, defining the dark current response values as a dark current response matrix with the number of color blocks on a reflection color card as the line number and the number of color camera channels as the column number, wherein each line represents the dark current response value of 3 channels of RGB corresponding to one color block, then measuring XYZ tristimulus values of all color blocks in a large-size reflection color card by using a spectrophotometer, and defining the dark current response values as a tristimulus matrix with the number of color blocks on the reflection color card as the line number and the number of color camera channels as the column number, wherein each line represents the XYZ tristimulus values corresponding to one color block;
(3) carrying out nonlinear correction on RGB response values by utilizing Y values in tristimulus values of neutral color blocks in a large-size reflection color card and corresponding RGB values thereof;
(4) establishing a forward chromaticity characterization model of the color digital microscope, namely a conversion relation between an RGB value and an XYZ tristimulus value;
(5) shooting and collecting RGB images of a target object, deducting dark current pixel by pixel and correcting nonlinearity of RGB response values to obtain a corrected response value matrix of the target object;
(6) and calculating the sRGB response value of the target object by using the tristimulus value of the target object to obtain an sRGB image which is the microscopic image after color correction.
The specific correction method comprises the following steps:
(1) turning on a lighting source of a color digital reflection microscope, preheating for a period of time, placing one color block of a large-size reflection color card below an upper ocular of an objective table, shooting the color block by using a color camera to obtain RGB values in an image of the color block, sequentially replacing other color blocks on the objective table to obtain RGB values of all color blocks in the large-size reflection color card, and recording the RGB values as RGB values
Figure BDA0002213838360000021
Representing P as a matrix with n rows and 3 columns, wherein n is the number of color blocks on a large-size reflective color card, 3 is the number of color camera channels, each row represents RGB 3 channel response values corresponding to one color block, and each column represents the response values of n color blocks output by one channel;
(2) turning off the microscope illumination light source and using the color camera to shoot, obtaining the dark current response value of the color camera and recording the dark current response value
Figure BDA0002213838360000022
D is a matrix with n rows and 3 columns, wherein n is the number of color blocks on the large-size reflective color card, each row represents the dark current response value of an RGB 3 channel corresponding to one color block, and the response value of the color block after subtracting the dark current is PDP-D, and then measuring the XYZ tristimulus values of all color blocks in the large-size reflection color card by using a spectrophotometer and recording the XYZ tristimulus values as
Figure BDA0002213838360000023
Expressing that S is a matrix with n rows and 3 columns, n is the number of color blocks on the large-size reflection color card, and each row expresses XYZ tristimulus values corresponding to one color block;
(3) respectively establishing RGB three-channel nonlinear correction relationship by using Y value in tristimulus value of neutral color block in large-size reflection color card and its corresponding RGB value, as shown in formula (1),
yk＝c1pik 3+c2pik 2+c3pik+c4
in the above formula, ykNormalized Y stimulus value, p, representing the k-th neutral color BlockikDenotes the normalized response value, c, of the kth neutral patch at the ith (i ∈ { R, G, B }) channel1，c2，c3，c4Fitting coefficients of cubic polynomials are respectively adopted, the values of the four fitting coefficients are respectively between 0 and 3 and cannot be simultaneously 0, and the RGB response values P of all color blocks in the large-size reflection color card are corrected by utilizing the formulaDAnd recording the corrected color block response value as
Figure BDA0002213838360000031
Represents PcA matrix of n rows and 3 columns;
(4) establishing forward chromaticity characterization model of color digital microscope, i.e. establishing conversion relation between RGB value and XYZ tristimulus value, and using corrected color block response value PcAnd the tristimulus value S establishes a conversion mathematical model between the tristimulus values S and the tristimulus value S, and the conversion matrix Z between the tristimulus values S and the tristimulus value S is calculated by a least square method, namely the formula (2)
Z＝STPc(Pc TPc)-1
Wherein, superscript T represents the transpose of the matrix, and superscript-1 represents the inverse of the matrix;
(5) using a color camera to shoot and collect RGB images of a target object, deducting dark current pixel by pixel and correcting nonlinearity of RGB response values according to a formula (1) to obtain a corrected target objectA matrix of response values of
Figure BDA0002213838360000032
Represents PtFor a matrix of m rows and 3 columns, m being the number of pixels in the image, the XYZ tristimulus values S of the target object are calculated using the conversion matrix Z in equation (2)tI.e. formula (3)
St＝ZPt；
(6) Utilizing tristimulus values S of target objectstCalculating the sRGB response value by formula (4), wherein sRGB is MStThe obtained sRGB image is a microscopic image after color correction, wherein the matrix
Figure BDA0002213838360000033
Compared with the prior art, the method has the advantages that the large-size reflection color chip is used for correcting the color of the microscopic image acquired by the color digital reflection microscope, and the illumination light source of the digital microscope is usually fixed, so that the color characterization of the digital camera under the fixed condition is particularly suitable for the color correction of the digital microscope, and the method utilizes the color block in the large-size reflection color chip as a training sample to establish a forward chromaticity characterization model of the color digital reflection microscope; the method can realize the accurate correction of the color of the shot target, improve the color accuracy of the color digital reflection microscope, accord with the color perception of human eyes, realize the effect of what you see is what you get, obviously improve the identification degree of the target object in the microscopic detection, and realize the accurate identification and positioning of the target object.
Drawings
FIG. 1 is a flow chart of the method for correcting color of a color digital reflection microscope.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
Taking a color Digital metallographic microscope as an example, a method for correcting the color of the color Digital metallographic microscope based on a large-size reflection color chart is described, wherein the large-size reflection color chart adopts a Digital Semi-Gloss color chart produced by Eseoli corporation, which is hereinafter referred to as a DSG color chart for short, the number of color blocks on the DSG color chart is 140, and the number of color camera channels is 3.
The method for correcting the color of the color digital reflection microscope based on the large-size reflection color chart specifically comprises the following steps:
(1) turning on a lighting source of a color digital metallographic microscope, preheating for a period of time, placing one color block of the DSG color card below an upper ocular of an objective table, shooting the color block by using a color camera to obtain RGB values in an image of the color block, sequentially replacing other color blocks on the objective table to obtain RGB values of all color blocks in the DSG color card, and recording the RGB values as a matrix of 140 rows and 3 columns
Figure BDA0002213838360000041
Each row represents the RGB 3 channel response value corresponding to one color block, and each column represents the response values of 140 color blocks output by one channel;
(2) turning off the microscope illumination light source and using the color camera to shoot, obtaining the dark current response value of the color camera and recording as a matrix
Figure BDA0002213838360000042
Each row represents the dark current response value of the RGB 3 channel corresponding to one color block, and the color block response value after deducting the dark current is PDThe XYZ tristimulus values of all color blocks in the DSG color card were measured using a spectrophotometer SP64 from Escheriski, and recorded as a matrix
Figure BDA0002213838360000043
Each row represents XYZ tristimulus values corresponding to one color patch;
(3) the RGB response values are corrected nonlinearly, the Y values in the tristimulus values of 6 neutral color blocks in the central area of the DSG color card and the corresponding RGB values are used to respectively establish the nonlinear correction relation of RGB three channels, as shown in the following formula,
yk＝c1pik 3+c2pik 2+c3pik+c4 (1)
in the above formula, ykNormalized Y stimulus values representing the k-th neutral patch, where k represents one of the 6 neutral patches of the central region on the color chip, pikDenotes the normalized response value, c, of the kth neutral patch at the ith (i ∈ { R, G, B }) channel1，c2，c3，c4The fitting coefficients of the cubic polynomials are respectively, the values of the four fitting coefficients are respectively between 0 and 3, and cannot be simultaneously 0, in this embodiment, c1 is 1.727, c2 is-2.095, c3 is 1.457, and c4 is-0.122 may be respectively taken, and the RGB response values P of all color patches in the DSG color patch are corrected by the above formulaDAnd the corrected color block response value is recorded as a matrix
Figure BDA0002213838360000051
(4) Establishing a forward chromaticity characterization model of a color digital microscope, i.e. establishing a conversion relation between RGB value and XYZ tristimulus values, and using corrected color block response value PcAnd the tristimulus values S of the three-dimensional transformation matrix are used for establishing a transformation mathematical model between the three-dimensional transformation matrix and the tristimulus values S, and a transformation matrix Z between the three-dimensional transformation matrix and the tristimulus values S is calculated by a least square method, namely
Z＝STPc(Pc TPc)-1 (2)
Wherein, superscript T represents the transpose of the matrix, and superscript-1 represents the inverse of the matrix;
(5) shooting and collecting RGB images of a metal sample, deducting dark current pixel by pixel and correcting nonlinearity of RGB response values according to formula (1) to obtain a response value matrix after correction of a target object
Figure BDA0002213838360000052
m is the number of pixels in the image, and the XYZ tristimulus values S of the metal sample are calculated by using the conversion matrix Z of the formula (2)tI.e. by
St＝ZPt (3)
(6) Using tristimulus values S of metal samplestCalculating the sRGB response value, as shown in formula (4),
sRGB＝MSt (4)
wherein the matrix
Figure BDA0002213838360000053
The obtained sRGB image is a microscopic image after color correction.

Claims (2)

1. A color correction method of a color digital reflection microscope is characterized by comprising the following steps:
(1) obtaining RGB values of all color blocks in a large-size reflection color card by using a color camera on a color digital reflection microscope, and defining the RGB values as an RGB matrix with the number of the color blocks on the reflection color card as a line number and the number of color camera channels as a column number, wherein each line represents 3-channel response values of RGB corresponding to one color block, and each column represents response values of all the color blocks output by one channel;
(2) the method comprises the steps of continuously shooting after an illumination light source of a microscope is turned off, obtaining dark current response values of a color camera, defining the dark current response values as a dark current response matrix with the number of color blocks on a reflection color card as the line number and the number of color camera channels as the column number, wherein each line represents the dark current response value of 3 channels of RGB corresponding to one color block, then measuring XYZ tristimulus values of all color blocks in a large-size reflection color card by using a spectrophotometer, and defining the dark current response values as a tristimulus matrix with the number of color blocks on the reflection color card as the line number and the number of color camera channels as the column number, wherein each line represents the XYZ tristimulus values corresponding to one color block;
(3) carrying out nonlinear correction on RGB response values by utilizing Y values in tristimulus values of neutral color blocks in a large-size reflection color card and corresponding RGB values thereof;
(4) establishing a forward chromaticity characterization model of the color digital microscope, namely a conversion relation between an RGB value and an XYZ tristimulus value;
(5) shooting and collecting RGB images of a target object, deducting dark current pixel by pixel and correcting nonlinearity of RGB response values to obtain a corrected response value matrix of the target object;
(6) and calculating the sRGB response value of the target object by using the tristimulus value of the target object to obtain an sRGB image which is the microscopic image after color correction.
2. The color correction method of a color digital reflection microscope according to claim 1, wherein the specific correction method is as follows:
(1) turning on a lighting source of a color digital reflection microscope, preheating for a period of time, placing one color block of a large-size reflection color card below an upper ocular of an objective table, shooting the color block by using a color camera to obtain RGB values in an image of the color block, sequentially replacing other color blocks on the objective table to obtain RGB values of all color blocks in the large-size reflection color card, and recording the RGB values as RGB values
Figure FDA0002213838350000011
Representing P as a matrix with n rows and 3 columns, wherein n is the number of color blocks on a large-size reflective color card, 3 is the number of color camera channels, each row represents RGB 3 channel response values corresponding to one color block, and each column represents the response values of n color blocks output by one channel;
(2) turning off the microscope illumination light source and using the color camera to shoot, obtaining the dark current response value of the color camera and recording the dark current response value
Figure FDA0002213838350000021
D is a matrix with n rows and 3 columns, wherein n is the number of color blocks on the large-size reflective color card, each row represents the dark current response value of an RGB 3 channel corresponding to one color block, and the response value of the color block after subtracting the dark current is PDP-D, and then measuring the XYZ tristimulus values of all color blocks in the large-size reflection color card by using a spectrophotometer and recording the XYZ tristimulus values as
Figure FDA0002213838350000022
Expressing that S is a matrix with n rows and 3 columns, n is the number of color blocks on the large-size reflection color card, and each row expresses XYZ tristimulus values corresponding to one color block;
(3) the Y value in the tristimulus values of the neutral color blocks in the large-size reflection color card and the corresponding RGB values are utilized to respectively establish the nonlinear correction relation of RGB three channels as shown in the following formula,
yk＝c1pik 3+c2pik 2+c3pik+c4
in the above formula, ykNormalized Y stimulus value, p, representing the k-th neutral color BlockikDenotes the normalized response value, c, of the kth neutral patch at the ith (i ∈ { R, G, B }) channel1，c2，c3，c4Fitting coefficients of cubic polynomials are respectively adopted, the values of the four fitting coefficients are respectively between 0 and 3 and cannot be simultaneously 0, and the RGB response values P of all color blocks in the large-size reflection color card are corrected by utilizing the formulaDAnd recording the corrected color block response value as
Figure FDA0002213838350000023
Represents PcA matrix of n rows and 3 columns;
(4) establishing forward chromaticity characterization model of color digital microscope, i.e. establishing conversion relation between RGB value and XYZ tristimulus value, and using corrected color block response value PcAnd the tristimulus values S of the three-dimensional transformation matrix are used for establishing a transformation mathematical model between the three-dimensional transformation matrix and the tristimulus values S, and a transformation matrix Z between the three-dimensional transformation matrix and the tristimulus values S is calculated by a least square method, namely
Z＝STPc(Pc TPc)-1
Wherein, superscript T represents the transpose of the matrix, and superscript-1 represents the inverse of the matrix;
(5) using a color camera to capture and acquire RGB images of a target object, subtracting dark current pixel by pixel and calculating the formula yn＝c1pin 3+c2pin 2+c3pin+c4Correcting the nonlinearity of the RGB response value to obtain a corrected response value matrix of the target object, and recording the corrected response value matrix as
Figure FDA0002213838350000024
Represents PtFor a matrix of m rows and 3 columns, m being the number of pixels in the image, XYZ tristimulus values S of the target object are calculated using a conversion matrix ZtI.e. by
St＝ZPt；
(6) Utilizing tristimulus values S of target objectstCalculating its sRGB response value, sRGB ═ MStThe obtained sRGB image is a microscopic image after color correction, wherein the matrix
Figure FDA0002213838350000031
CN201910907891.1A 2019-09-25 2019-09-25 Color correction method for color digital reflection microscope Active CN110726536B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910907891.1A CN110726536B (en) 2019-09-25 2019-09-25 Color correction method for color digital reflection microscope

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910907891.1A CN110726536B (en) 2019-09-25 2019-09-25 Color correction method for color digital reflection microscope

Publications (2)

Publication Number Publication Date
CN110726536A CN110726536A (en) 2020-01-24
CN110726536B true CN110726536B (en) 2021-08-06

Family

ID=69218347

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910907891.1A Active CN110726536B (en) 2019-09-25 2019-09-25 Color correction method for color digital reflection microscope

Country Status (1)

Country Link
CN (1) CN110726536B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116612199B (en) * 2023-07-20 2024-02-06 中国科学院光电技术研究所 On-orbit combined color calibration method for deep space exploration color camera

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105651386A (en) * 2016-03-04 2016-06-08 温州佳易仪器有限公司 Method for carrying out color testing on standard light source color assessment inspection box
CN106153193A (en) * 2016-07-22 2016-11-23 浙江大学 A kind of method utilizing the double light source response value of multispectral camera to obtain spectral reflectance
CN107197225A (en) * 2017-06-13 2017-09-22 浙江大学 Color digital camera white balance correcting based on chromatic adaptation model
CN109218698A (en) * 2018-10-19 2019-01-15 浙江大学 A kind of color digital camera color calibration method of high fault tolerance
US20190072751A1 (en) * 2017-09-04 2019-03-07 Microscopes International, Llc Systems and methods for detection of blank fields in digital microscopes
CN109903256A (en) * 2019-03-07 2019-06-18 京东方科技集团股份有限公司 Model training method, chromatic aberration calibrating method, device, medium and electronic equipment

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104168475B (en) * 2014-08-15 2016-02-03 浙江大学 A kind of imaging type of digital camera changeable parameters obtains the method for color tristimulus values

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105651386A (en) * 2016-03-04 2016-06-08 温州佳易仪器有限公司 Method for carrying out color testing on standard light source color assessment inspection box
CN106153193A (en) * 2016-07-22 2016-11-23 浙江大学 A kind of method utilizing the double light source response value of multispectral camera to obtain spectral reflectance
CN107197225A (en) * 2017-06-13 2017-09-22 浙江大学 Color digital camera white balance correcting based on chromatic adaptation model
US20190072751A1 (en) * 2017-09-04 2019-03-07 Microscopes International, Llc Systems and methods for detection of blank fields in digital microscopes
CN109218698A (en) * 2018-10-19 2019-01-15 浙江大学 A kind of color digital camera color calibration method of high fault tolerance
CN109903256A (en) * 2019-03-07 2019-06-18 京东方科技集团股份有限公司 Model training method, chromatic aberration calibrating method, device, medium and electronic equipment

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Colour management system for displaying microscope images;Kaida Xiao等;《Displays》;20121031;第33卷(第4-5期);214-220 *
基于多光谱成像技术的光谱图像重构与显示研究;徐鹏;《中国博士学位论文全文数据库 信息科技辑》;20180315(第3期);I138-20 *
用于数码显微镜的白平衡校正方法;窦江龙;《光子学报》;20120430;第41卷(第4期);472-477 *

Also Published As

Publication number Publication date
CN110726536A (en) 2020-01-24

Similar Documents

Publication Publication Date Title
CN105960586B (en) System and method for image acquisition using supervised high quality imaging
CN105187819B (en) A kind of industry color camera color response test and means for correcting and method
CN101933321A (en) Image sensor apparatus and method for scene illuminant estimation
US9270866B2 (en) Apparatus and method for automated self-training of white balance by electronic cameras
CN110110131B (en) Airplane cable support identification and parameter acquisition method based on deep learning and binocular stereo vision
CN113676628A (en) Multispectral sensor, imaging device and image processing method
CN107563987A (en) Demarcate the method and device of imaging difference
CN109242792B (en) White balance correction method based on white object
CN110702615B (en) Color correction method for color digital transmission microscope
CN101964293A (en) Metallographical microstructural image processing method
CN113628135A (en) Image color correction method, image color correction device, computer device, and storage medium
CN113873229A (en) Image dead pixel detection method, system and device
CN110726536B (en) Color correction method for color digital reflection microscope
CN115665565A (en) Online tobacco leaf image color correction method, system and device
CN106296658B (en) A kind of scene light source estimation accuracy method for improving based on camera response function
CN112488997B (en) Method for detecting and evaluating color reproduction of ancient painting printed matter based on characteristic interpolation
JP2015194567A (en) display device
CN106023238A (en) Color data calibration method for camera module
US8169475B2 (en) Image processing system, imaging system, and microscope imaging system
CN111010557B (en) White point range determining method, white balance correcting method and device, and storage medium
CN117115610A (en) Metal surface defect detection method and system based on multi-angle image exposure fusion
US20070126894A1 (en) Method for calculating color correction
CN111896109B (en) Spectrum reconstruction method based on original response value of digital camera
CN114972065A (en) Training method and system of color difference correction model, electronic equipment and mobile equipment
EP2204980B1 (en) Image processing system, imaging system, and microscope imaging system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant