CN108896499A - In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization - Google Patents

In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization Download PDF

Info

Publication number
CN108896499A
CN108896499A CN201810437301.9A CN201810437301A CN108896499A CN 108896499 A CN108896499 A CN 108896499A CN 201810437301 A CN201810437301 A CN 201810437301A CN 108896499 A CN108896499 A CN 108896499A
Authority
CN
China
Prior art keywords
training sample
regularization
principal component
polynomial
matrix
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.)
Pending
Application number
CN201810437301.9A
Other languages
Chinese (zh)
Inventor
王慧琴
王可
王展
王伟超
赵丽娟
杨蕾
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.)
Xian University of Architecture and Technology
Original Assignee
Xian University of Architecture and Technology
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 Xian University of Architecture and Technology filed Critical Xian University of Architecture and Technology
Priority to CN201810437301.9A priority Critical patent/CN108896499A/en
Publication of CN108896499A publication Critical patent/CN108896499A/en
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry

Landscapes

  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Spectrometry And Color Measurement (AREA)

Abstract

The invention discloses a kind of combination principal component analysis and the polynomial spectral reflectance recovery method of regularization, include the following steps：1) using the spectroscopic data of multi-optical spectrum imaging system acquisition training sample；2) dimensionality reduction is carried out using spectroscopic data of the principal component analytical method to training sample, to reduce the spectroscopic data amount of training sample；3) training sample set is constructed by the spectroscopic data for handling obtained training sample through step 2), the extension of polynomial regression channel response is carried out to training sample set again, then construct the spectral reflectivity to reconstruct and between actual value the minimum target of error objective function, and limit entry is added into objective function using Tikhonov regularization method, finally solve the objective function, the spectral reflectivity that must be rebuild, it completes to combine principal component analysis and the polynomial spectral reflectance recovery of regularization, this method can rebuild to obtain accurate spectral reflectivity.

Description

In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization
Technical field
The invention belongs to digital image processing field, it is related to a kind of combination principal component analysis and the polynomial spectrum of regularization Reflectivity method for reconstructing.
Background technique
The method that tradition obtains object spectra reflectivity is using the point-to-point measurement of spectrophotometer, and workload is very Greatly, due to people life in most of body surfaces spectral reflectivity be it is smooth, in order to be efficiently obtained body surface Spectral reflectivity can use multispectral colouring information of the multi-optical spectrum imaging system acquisition object under multiple channels, reuse Algorithm for reconstructing efficiently reappears the continuous spectrum of body surface, and this method is known as the spectral reflectance based on multi-optical spectrum imaging technology Rate method for reconstructing, this method can obtain the information of target object from spectral Dimensions and Spatial Dimension simultaneously.However, traditional colour Degree Spatial Dimension only has 1/10th of the dimension of spectral space, when indicating color, needs to handle using spectral reflectivity big The spectroscopic data of amount, so that treatment effeciency is low, memory space increases, therefore, can be to big under conditions of meeting reconstruction precision It measures spectroscopic data and carries out data compression, to achieve the effect that spectrum dimensionality reduction.Currently, common spectroscopic data by dimension method have it is main at Divide the related algorithm of analysis method, independent principal component analytical method and other improvements, however above method cannot reconstruct standard True spectral reflectivity.
Summary of the invention
It is an object of the invention to overcome the above-mentioned prior art, a kind of combination principal component analysis and canonical are provided Change polynomial spectral reflectance recovery method, this method can rebuild to obtain accurate spectral reflectivity.
In order to achieve the above objectives, combination principal component analysis of the present invention and the polynomial spectral reflectivity weight of regularization Construction method includes the following steps：
1) using the spectroscopic data of multi-optical spectrum imaging system acquisition training sample；
2) dimensionality reduction is carried out using spectroscopic data of the principal component analytical method to training sample, to reduce the spectrum of training sample Data volume；
3) training sample set is constructed by the spectroscopic data for handling obtained training sample through step 2), then to training sample Set carries out the extension of polynomial regression channel response, then constructs the spectral reflectivity to reconstruct and error is most between actual value The small objective function for target, and limit entry is added into objective function using Tikhonov regularization method, finally solve institute Objective function is stated, the spectral reflectivity of reconstruction is obtained, completes to combine principal component analysis and the polynomial spectral reflectivity weight of regularization It builds.
Multi-optical spectrum imaging system in step 1) is SpectroCam VIS CCD monochrome cameras.
The specific operation process of step 3) is：
1a) obtain the channel response g of multispectral camera and matched i channel filteri, i.e.,
Wherein, l (λ), fi(λ), o (λ) and s (λ) are function of spectral power distribution, the multi-optical spectrum imaging system of lighting source The spectrum sensitive function of the transmissivity of i-th of channel filter, the spectral transfer function of camera lens and multispectral camera；
2a) with k orthogonal basis vectors biLinear combination be approximately by spectral reflectance data collection R：
Wherein, B=[b1,b2,Λ,bk] it is characterized vector matrix, a=[a1,a2,Λ,ak]TFor transition matrix；
3a) the corresponding channel response g of each color cardiFor gi=[g1,g2,Λ,gC]T, utilize polynomial regression pair Original channel response giCarry out higher order polynomial multiplication extension, wherein giResult after extensionFor：
WithThe matrix for indicating m × K dimension, obtains transition matrix Q+For：
Wherein, using polynomial regression to original channel response giThe purpose for carrying out higher order polynomial multiplication extension exists In keeping the difference between the spectral reflectivity reconstructed and actual value minimum, that is, construct objective function：
E||R-Q+g||2→Minimum (5)
Wherein, E is mean square deviation, | | | |2For L2Norm, Minimum are to minimize；
4a) using Tikhonov regularization method in formula (5) | | R-Q+G | | middle addition limit entry then adds limitation Objective function is after：
Wherein, λ is regularization parameter；
5a) according to the limit entry of addition to matrixConditional number Cond changed accordingly, and by matrixChange ForSo that matrixCharacteristic value can fall inIn range, wherein matrixItem Number of packages meets：
6a) withAnd lg | | Q+| | it is respectively the abscissa and ordinate of L-curve, when λ makesAnd lg | | Q+| | while when obtaining sufficiently small, then correspond to the angle point of the L-curve, wherein the angle point is L bent The maximum curvature point of line, ifη (λ)=| | Q+| |, then L-curve curvature K (λ) calculation formula is：
As d [K (λ)]/d λ=0, curvature K (λ) is maximized, and obtains optimal regularization parameter λ；
7a) the optimal regularization parameter λ for calculating step 6a) is substituted into formula (6), is then solved objective function, is obtained The spectral reflectivity of reconstruction.
The invention has the advantages that：
Combination principal component analysis of the present invention is specifically being grasped with the polynomial spectral reflectance recovery method of regularization When making, using the spectroscopic data of multi-optical spectrum imaging system acquisition training sample, recycle principal component analytical method to training sample Spectroscopic data carry out dimensionality reduction, to reduce the data volume of training sample, then to training sample set carry out polynomial regression Channel response extension, then objective function is constructed, and limit entry is added into objective function using Tikhonov regularization method, lead to Crossing limit entry, data are unstable and random noise bring ill-conditioning problem limiting, to reduce in multispectral data treatment process Data volume achievees the purpose that the precision for improving the spectral reflectivity rebuild.
Detailed description of the invention
Fig. 1 is mural painting reference color block label figure in emulation experiment；
Fig. 2 is the multispectral image figure of mural painting in emulation experiment；
Fig. 3 a is the reconstruction result map of 1 position in Fig. 1；
Fig. 3 b is the reconstruction result map of 2 positions in Fig. 1；
Fig. 3 c is the reconstruction result map of 3 positions in Fig. 1；
Fig. 3 d is the reconstruction result map of 4 positions in Fig. 1；
Fig. 3 e is the reconstruction result map of 5 positions in Fig. 1；
Fig. 3 f is the reconstruction result map of 6 positions in Fig. 1.
Specific embodiment
The invention will be described in further detail with reference to the accompanying drawing：
Combination principal component analysis of the present invention includes following with the polynomial spectral reflectance recovery method of regularization Step：
1) using the spectroscopic data of multi-optical spectrum imaging system acquisition training sample, specifically, passing through multi-optical spectrum imaging system The image information of picture to be processed is acquired, and using collected information as the spectroscopic data of training sample；
2) dimensionality reduction is carried out using spectroscopic data of the principal component analytical method to training sample, to reduce the spectrum of training sample Data volume；
3) training sample set is constructed by the spectroscopic data for handling obtained training sample through step 2), then to training sample Set carries out the extension of polynomial regression channel response, then constructs the spectral reflectivity to reconstruct and error is most between actual value The small objective function for target, and limit entry is added into objective function using Tikhonov regularization method, finally solve institute Objective function is stated, the spectral reflectivity of reconstruction is obtained, completes to combine principal component analysis and the polynomial spectral reflectivity weight of regularization It builds.
Multi-optical spectrum imaging system in step 1) is SpectroCam VIS CCD monochrome cameras.
The concrete operations of step 3) are：
The spectral reflectivity of object 1a) is rebuild using rebuilding spectrum algorithm based on multi-optical spectrum imaging technology, is obtained first more The digital response g of spectrum camera and matched i channel filteri, wherein
Wherein, l (λ), fi(λ), o (λ) and s (λ) are respectively the function of spectral power distribution of lighting source, multispectral imaging The spectrum sensitive function of the transmissivity of i-th of channel filter, the spectral transfer function of camera lens and multispectral camera in system. Construct single spectral function qi(λ)=l (λ) fi(λ) o (λ) s (λ), biAnd niThe dark current respectively represented under i-th of optical filter is rung Answer and system generate noise.After being sampled according to N point into row interval to the spectral reflectivity in visible-range 380-780nm, The vector R that will obtain a N × 1 dimension indicates that the camera channel of the dimensional vector of C × 1 responds with g, will using vector matrix form Formula (1) is rewritten as：
G=QR+b+n (2)
Wherein, Q is C × N-dimensional spectral response matrix, and formula (2) is rewritten as G=QR+n using G=g-b, passes through mark Quasi- blank correction removal dark current b, wherein the updating formula for removing dark current is：
Wherein, GwhiteFor the response of standard white plate, GdataFor the real response of system, GdarkTo use lens cap to block phase System response after machine camera lens；The influence of noise n is not considered after removing system dark current, then the basic conversion shape of formula (2) Formula is：
G=QR (4)
2a) with k orthogonal basis vectors biLinear combination spectral reflectance data collection R is approximately indicated For：
Wherein, B=[b1,b2,Λ,bk] it is characterized vector matrix, a=[a1,a2,Λ,ak]TFor transition matrix, base vector Principal component analytical method can be used to be calculated, it is assumed that N*M ties up matrix R=(R1,R2,Λ,Rm)TBy M unequal spectrum Reflectivity composition carries out singular value decomposition (SVD) to matrix R, then has：
R=USVT (6)
Wherein, S is diagonal matrix, is made of a singular value arranged from big to small of r=rank (R), i.e.,
S=diag (σ1,σ2,Λ,σi) (7)
By RRTFeature vector composition N*N tie up unit orthogonal matrix U, RTThe unit matrix of the feature vector composition M*M dimension of R The linear combination of V, former spectral reflectance data are indicated by preceding k principal component, wherein the number of principal component is contributed by principal component Rate determines that the principal component accumulation contribution rate of preceding k feature vector is：
Wherein, when principal component number meets the preceding k accumulation contribution rate ρ of acquirement spectral reflectivityk>When 99.95%, then will Formula (5) is substituted into formula (4) and is obtained：
g0=QBa0 (9)
Wherein, g0For the channel response of training sample, Ba0For the spectral reflectivity collection R of training sample0Feature vector square Battle array and transition matrix, QB are the response values of preceding k feature vector principal component, obtain coefficient transition matrix a by formula (9)0 For：
a0=[(QB)T(QB)]-1(QB)Tg0 (10)
The value of QB can be found out according to training sample in practice, wherein
Meanwhile the value of QB changes with system response, if using new test sample, coefficient transition matrix can be with It is expressed as：
Wherein, g is the channel response of test sample, the then spectral reflectivity rebuild using principal component analytical methodFor：
3a) for each color card, corresponding channel response contains C element, i.e. gi=[g1,g2,Λ,gC]T, lead to In normal situation, number of active lanes is more, and the linearity of imaging system is better, but the filter disc of multi-optical spectrum imaging system can not be without limitation Increase.Under the premise of not increasing the number of optical filter, for the linearity for improving system, polynomial regression pair can use Original channel response carries out higher order polynomial multiplication extension, i.e. result after gi extension is
WithIndicate that (m is a m × KThe number of middle element) dimension matrix, obtain new transition matrix Q+For：
Use the difference that the purpose that polynomial regression is extended is between the spectral reflectivity for making to reconstruct and actual value Minimum, i.e.,：
E||R-Q+g||2→Minimum (16)
Wherein, | | | |2Represent L2Norm.
4a) to solve ill ill-posed problem, Tikhonov regularization method can be used, limit is added in objective function Item processed allows the minimization problem of the objective function after limit entry is added suitable fixed, wherein addition limitation from the angle of optimum theory Objective function is changed into after condition：
Previous item in formula (17) guarantees the authenticity of spectroscopic data, the flatness of latter control solution, and λ is regularization ginseng Number adjusts the weight relationship between data validity and the flatness of solution.
By formula (4) it is found that the precision of spectral reflectance recovery depends on transition matrix Q+, Q+It is the reflection by training sample What rate matrix R and response matrix g was obtained, therefore, from the aspect of spectroscopic data and channel response two, using dimensionality reduction and regularization Method solve ill-condition equation simultaneously, it is necessary first to the spectral signature vector B of spectroscopic data is extracted using principal component analytical method, Channel response and in this, as the input variable of training sample, after extension is then adjusted using regularization Controlling object functionAnd in this, as the output variable of training sample, wherein be added to Tikhonov restrictive condition and principal component analysis dimensionality reduction Transition matrix can change：
Wherein, I is unit matrix, and formula (18) are carried out singular value decomposition, are obtained：
The stability of system can be generally measured by the conditional number of matrix, the conditional number of matrix is defined as：
Wherein, σmaxAnd σminFor matrixMaximum singular value and minimum singular value.
After restrictive condition 5a) is added using Tikhonov method, matrixConditional number Cond needs become accordingly Change, because of matrixFor a positive semidefinite symmetrical matrix, matrixIt is changed toSo that matrixSpy Value indicative can be fallen inIn range, thus restrictive condition coefficient, it is ensured that conditional number can obtain minimum value, i.e., its Number of packages meets：
Formula (21) shows that conditional number can be controlled by regularization parameter λ, the disease bigger for those conditional numbers State matrix, so that it may best λ be chosen by L-curve method to reduce its ill-conditioning problem, wherein the finding process of best λ is：
With | | Q+| | it is the function of regularization parameter λ, withAnd lg | | Q+| | it is respectively The abscissa and ordinate of L-curve, when λ makesAnd lg | | Q+| | while when obtaining sufficiently small, then correspond to L song Line angle point, and the angle point is L-curve point of maximum curvature, usually best λ is determined by the maximum point, if settingη (λ)=| | Q+| |, then L-curve curvature K (λ) calculation formula is：
As d [K (λ)]/d λ=0, curvature K (λ) is maximized, and this value is optimal regularization parameter λ.
Emulation experiment
In order to verify the application effect of (PTP method) of the invention in a practical situation, ratio is chosen for the true mural painting of a width More typical six color reference regions are studied, and mural painting color lump chosen area is obtained by multispectral system acquisition mural painting Multispectral image under 11 channels, analysis PTP method carry out the precision of rebuilding spectrum.It is with reference to color lump with six in mural painting Test sample carries out rebuilding spectrums using tri- kinds of methods of PCA, RPR and PTP, six color lump spectral accuracies that PTP method is rebuild with PCA with RPR method is compared, and average RMSE value, average GFC value, average ISSD value have apparent reduction, using PTP method into Row spectral reflectance recovery can obtain preferable spectral accuracy.In order to accurately show the reference of PCA, RPR and PTP method reconstruction The coloration precision of color lump, by mural painting with reference to the L in color lump coloration*, a*, b*Component value calculates, L*For brightness, a*To be red green, b*For Champac.It can be concluded that original chrominance of the color lump chromatic value of PTP method reconstruction closer to color lump, the present invention can be compared The better coloration precision of PCA and RPR method, wherein 1 comparing result of table.
Table 1

Claims (3)

1. a kind of combination principal component analysis and the polynomial spectral reflectance recovery method of regularization, which is characterized in that including with Lower step：
1) using the spectroscopic data of multi-optical spectrum imaging system acquisition training sample；
2) dimensionality reduction is carried out using spectroscopic data of the principal component analytical method to training sample, to reduce the spectroscopic data of training sample Amount；
3) training sample set is constructed by the spectroscopic data for handling obtained training sample through step 2), then to training sample set The extension of polynomial regression channel response is carried out, the spectral reflectivity to reconstruct then is constructed and error is minimum between actual value The objective function of target, and limit entry is added into objective function using Tikhonov regularization method, finally solve the mesh Scalar functions obtain the spectral reflectivity of reconstruction, complete to combine principal component analysis and the polynomial spectral reflectance recovery of regularization.
2. combination principal component analysis according to claim 1 and the polynomial spectral reflectance recovery method of regularization, It is characterized in that, the multi-optical spectrum imaging system in step 1) is SpectroCam VIS CCD monochrome cameras.
3. combination principal component analysis according to claim 1 and the polynomial spectral reflectance recovery method of regularization, It is characterized in that, the specific operation process of step 3) is：
1a) obtain the channel response g of multispectral camera and matched i channel filteri, i.e.,
Wherein, l (λ), fi(λ), o (λ) and s (λ) are i-th of function of spectral power distribution, multi-optical spectrum imaging system of lighting source The spectrum sensitive function of the transmissivity of channel filter, the spectral transfer function of camera lens and multispectral camera；
2a) with k orthogonal basis vectors biLinear combination be approximately by spectral reflectance data collection R：
Wherein, B=[b1,b2,Λ,bk] it is characterized vector matrix, a=[a1,a2,Λ,ak]TFor transition matrix；
3a) the corresponding channel response g of each color cardiFor gi=[g1,g2,Λ,gC]T, using polynomial regression to original Channel response giCarry out higher order polynomial multiplication extension, wherein giResult after extensionFor：
WithThe matrix for indicating m × K dimension, obtains transition matrix Q+For：
Wherein, using polynomial regression to original channel response giThe purpose for carrying out higher order polynomial multiplication extension is to make Difference between the spectral reflectivity reconstructed and actual value is minimum, i.e. building objective function：
E||R-Q+g||2→Minimum (5)
Wherein, E is mean square deviation, | | | |2For L2Norm, Minimum are to minimize；
4a) using Tikhonov regularization method in formula (5) | | R-Q+G | | middle addition limit entry then adds mesh after limit entry Scalar functions are：
Wherein, λ is regularization parameter；
5a) according to the limit entry of addition to matrixConditional number Cond changed accordingly, and by matrixIt is changed toSo that matrixCharacteristic value can fall inIn range, wherein matrixCondition Number meets：
6a) withAndThe respectively abscissa and ordinate of L-curve, when λ makesAnd lg||Q+| | while when obtaining sufficiently small, then correspond to the angle point of the L-curve, wherein the angle point is the maximum curvature of L-curve Point, ifη (λ)=| | Q+| |, then L-curve curvature K (λ) calculation formula is：
As d [K (λ)]/d λ=0, curvature K (λ) is maximized, and obtains optimal regularization parameter λ；
7a) the optimal regularization parameter λ for calculating step 6a) is substituted into formula (6), is then solved objective function, must be rebuild Spectral reflectivity.
CN201810437301.9A 2018-05-09 2018-05-09 In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization Pending CN108896499A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810437301.9A CN108896499A (en) 2018-05-09 2018-05-09 In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810437301.9A CN108896499A (en) 2018-05-09 2018-05-09 In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization

Publications (1)

Publication Number Publication Date
CN108896499A true CN108896499A (en) 2018-11-27

Family

ID=64343661

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810437301.9A Pending CN108896499A (en) 2018-05-09 2018-05-09 In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization

Country Status (1)

Country Link
CN (1) CN108896499A (en)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109709085A (en) * 2019-01-25 2019-05-03 厦门大学 A kind of multichannel Raman spectrum method for reconstructing, terminal device and storage medium
CN110736542A (en) * 2019-10-28 2020-01-31 南京林业大学 spectrum reconstruction method based on RGB values
CN111126452A (en) * 2019-12-03 2020-05-08 中国科学院国家空间科学中心 Ground feature spectral curve expansion method and system based on principal component analysis
CN111460966A (en) * 2020-03-27 2020-07-28 中国地质大学（武汉） Hyperspectral remote sensing image classification method based on metric learning and neighbor enhancement
CN111651720A (en) * 2020-06-11 2020-09-11 北京理工大学 Multispectral reconstruction method and device based on L4 norm optimization
CN111795742A (en) * 2020-05-18 2020-10-20 南京林业大学 Dimension reduction method for single RGB image reconstruction spectrum
CN112101381A (en) * 2020-08-30 2020-12-18 西南电子技术研究所（中国电子科技集团公司第十研究所） Tensor collaborative drawing discriminant analysis remote sensing image feature extraction method
CN112288721A (en) * 2020-10-29 2021-01-29 四川九洲电器集团有限责任公司 Mosaic multispectral image generation method for target detection
CN113792710A (en) * 2021-11-15 2021-12-14 滨州学院 Spectrum reconstruction method and device and electronic equipment
WO2022012668A1 (en) * 2020-07-17 2022-01-20 华为技术有限公司 Training set processing method and apparatus
CN114666523A (en) * 2022-03-30 2022-06-24 湖南格为科技有限公司 Energy-saving liquid crystal television capable of automatically adapting to human eye brightness
CN115187870A (en) * 2022-09-13 2022-10-14 浙江蓝景科技有限公司杭州分公司 Marine plastic waste material identification method and system, electronic equipment and storage medium
EP4174459A1 (en) * 2021-10-22 2023-05-03 Samsung Electronics Co., Ltd. Device and method for processing spectrum data of image sensor

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102871646A (en) * 2012-08-16 2013-01-16 清华大学 Large-data-size fluorescent molecule tomography reconstruction method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102871646A (en) * 2012-08-16 2013-01-16 清华大学 Large-data-size fluorescent molecule tomography reconstruction method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KE WANG 等: "Study of spectral reflectance reconstruction based on regularization matrix R method", 《CLUSTER COMPUT》 *
张伟峰: "基于支持向量回归的光谱反射率重建方法", 《计算机科学》 *
王可 等: "基于降维正则化多项式的光谱反射率重建方法", 《激光与光电子学进展》 *

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109709085A (en) * 2019-01-25 2019-05-03 厦门大学 A kind of multichannel Raman spectrum method for reconstructing, terminal device and storage medium
CN110736542A (en) * 2019-10-28 2020-01-31 南京林业大学 spectrum reconstruction method based on RGB values
CN110736542B (en) * 2019-10-28 2021-07-16 南京林业大学 Spectral reconstruction method based on RGB value
CN111126452A (en) * 2019-12-03 2020-05-08 中国科学院国家空间科学中心 Ground feature spectral curve expansion method and system based on principal component analysis
CN111126452B (en) * 2019-12-03 2023-05-23 中国科学院国家空间科学中心 Feature spectrum curve expansion method and system based on principal component analysis
CN111460966A (en) * 2020-03-27 2020-07-28 中国地质大学（武汉） Hyperspectral remote sensing image classification method based on metric learning and neighbor enhancement
CN111460966B (en) * 2020-03-27 2024-02-02 中国地质大学（武汉） Hyperspectral remote sensing image classification method based on metric learning and neighbor enhancement
CN111795742A (en) * 2020-05-18 2020-10-20 南京林业大学 Dimension reduction method for single RGB image reconstruction spectrum
CN111651720A (en) * 2020-06-11 2020-09-11 北京理工大学 Multispectral reconstruction method and device based on L4 norm optimization
CN111651720B (en) * 2020-06-11 2023-03-31 北京理工大学 Multispectral reconstruction method and device based on L4 norm optimization
WO2022012668A1 (en) * 2020-07-17 2022-01-20 华为技术有限公司 Training set processing method and apparatus
CN112101381B (en) * 2020-08-30 2022-10-28 西南电子技术研究所（中国电子科技集团公司第十研究所） Tensor collaborative drawing discriminant analysis remote sensing image feature extraction method
CN112101381A (en) * 2020-08-30 2020-12-18 西南电子技术研究所（中国电子科技集团公司第十研究所） Tensor collaborative drawing discriminant analysis remote sensing image feature extraction method
CN112288721A (en) * 2020-10-29 2021-01-29 四川九洲电器集团有限责任公司 Mosaic multispectral image generation method for target detection
EP4174459A1 (en) * 2021-10-22 2023-05-03 Samsung Electronics Co., Ltd. Device and method for processing spectrum data of image sensor
JP2023063240A (en) * 2021-10-22 2023-05-09 三星電子株式会社 Device and method for processing spectrum data of image sensor
JP7429931B2 (en) 2021-10-22 2024-02-09 三星電子株式会社 Apparatus and method for processing spectral data of image sensor
US11962921B2 (en) 2021-10-22 2024-04-16 Samsung Electronics Co., Ltd. Device and method for processing spectrum data of image sensor
KR102692570B1 (en) * 2021-10-22 2024-08-06 삼성전자주식회사 Apparatus and method for processing spectrum data of image sensor
CN113792710A (en) * 2021-11-15 2021-12-14 滨州学院 Spectrum reconstruction method and device and electronic equipment
CN114666523A (en) * 2022-03-30 2022-06-24 湖南格为科技有限公司 Energy-saving liquid crystal television capable of automatically adapting to human eye brightness
CN114666523B (en) * 2022-03-30 2023-10-27 湖南格为科技有限公司 Energy-saving liquid crystal television capable of automatically adapting to human eye brightness
CN115187870A (en) * 2022-09-13 2022-10-14 浙江蓝景科技有限公司杭州分公司 Marine plastic waste material identification method and system, electronic equipment and storage medium

Similar Documents

Publication Publication Date Title
CN108896499A (en) In conjunction with principal component analysis and the polynomial spectral reflectance recovery method of regularization
Jiang et al. What is the space of spectral sensitivity functions for digital color cameras?
Nguyen et al. Training-based spectral reconstruction from a single RGB image
CN109187378B (en) Hyperspectral image-based nondestructive detection method for content of soluble solids in kiwi fruit
CN105069234B (en) The spectrum dimension reduction method and system of a kind of view-based access control model Perception Features
WO2020199205A1 (en) Hybrid hyperspectral image reconstruction method and system
CN106471567A (en) Color calibration system and method
US8599380B2 (en) Method of constructing light-measuring look-up table, light-measuring method, and light-measuring system
US10578487B2 (en) Calibration for fabry perot spectral measurements
CN112834051B (en) Radiation temperature measurement method based on multispectral camera
CN110736542B (en) Spectral reconstruction method based on RGB value
US11455535B2 (en) Systems and methods for sensor-independent illuminant determination
CN110017904A (en) A kind of multispectral thermometry method based on CCD camera
JP2019020311A (en) Color measurement method and color measurement device
CN104062010B (en) A kind of light splitting light source colour illumination photometry instrument optimizing scaling algorithm
CN106153193B (en) A kind of method for obtaining spectral reflectance using the double light source responses of multispectral camera
CN106153192B (en) A kind of method that spectral reflectance is obtained using multispectral camera virtual responsive value
CN110660112A (en) Drawing spectrum reconstruction method based on special color card and multispectral imaging
KR101705818B1 (en) Apparatus, system and method for measuring luminance and chromaticity
CN106895916B (en) A kind of method that single exposure shooting obtains multispectral image
CN115731191A (en) Narrow-band spectral imaging method based on neural network
CN109738067A (en) A method of estimation narrow-band multispectral camera synthetic spectrum sensitivity
CN113790798A (en) Seamless spectral imaging device, system and method for dynamic point target tracking measurement
WO2016065856A1 (en) Light radiation measurement method based on light filtering unit and apparatus thereof
CN113298884B (en) RGB camera spectral sensitivity calibration method based on spectrum-adjustable LED light source

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20181127

RJ01 Rejection of invention patent application after publication