光谱学与光谱分析 |
|
|
|
|
|
Spectral Reflectance Reconstruction with Nonlinear Composite Model of the Metameric Black |
WANG Jia-jia1, LIAO Ning-fang1*, WU Wen-min1, CAO Bin1, LI Ya-sheng1, CHENG Hao-bo2 |
1. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China 2. Shenzhen Research Institute, Beijing Institute of Technology, Shenzhen 518057, China |
|
|
Abstract Metamerism phenomenon is an important problem in spectral reflectance reconstruction and color reproduction. In this paper, a 3-primary color CCD camera is used to acquire spectral information in CIE standard illuminant D65 and a nonlinear composite model is established, including principal component analysis and neural network method (PCA-NET) to modify the Matrix R Method based on the Metameric Black theory. The standard Munsell color card is used in spectral reflectance reconstruction experiment and the results are evaluated and discussed. The experimental results verified that the PCA-NET algorithm can accurately fit the nonlinear relationship between the output signal of the camera and the principal component coefficients; and it can be used in the R matrix algorithm instead of the linear algorithm; the new method can serve as a promising technique for building a spectral image database whihc is better than the original Matrix R Method. In the fixed illumination environment, the mean RMS of the test set is 0.76 improved, and the mean STD of the test set is 0.85 improved, which can effectively improve the accuracy of spectral reflectance reconstruction. The modified matrix R method has the advantages of higher accuracy and easy implementation, and it can be used in the field of color reproduction and spectral reflectance reconstruction.
|
Received: 2016-02-26
Accepted: 2016-06-08
|
|
Corresponding Authors:
LIAO Ning-fang
E-mail: liaonf@bit.edu.cn
|
|
[1] TANG Shun-qing(汤顺清). Colorimetry(色度学). Beijing: Beijing Institute of Technology Press(北京: 北京理工大学出版社),1991. 35. [2] Wyszecki G, Stiles W. Color Science. Second Edition, John Wiley & Sons, 1982. [3] Cohen B J, Kapauf E W. Am. J. Psychol.,1982, 95: 537. [4] Cohen B J. Color Research & Applications, 1988, 13: 5. [5] Zhao Y, Berns R S. Color Research & Application, 2007, 32(5): 343. [6] LIU Zhen, WAN Xiao-xia, HUANG Xin-guo, et al(刘 振, 万晓霞, 黄新国, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析). 2013, 33(4): 1076. [7] WANG Ying, WANG Zhong-min, WANG Yi-feng, et al(王 莹, 王忠民, 王义峰, 等). Optics and Precision Engineering(光学精密工程),2011, 19(5): 1171. [8] HE Song-hua, LIU Zhen, CHEN Qiao(何颂华, 刘 真, 陈 桥). Acta Optica Sinica(光学学报), 2014, 34(2): 0233001-1. [9] ZHOU Feng, PAN He-ping, DU Zhi-shun, et al(周 峰, 潘和平, 杜志顺, 等). Computing Techniques for Geophysical and Geochemical Exploration(物探化探计算技术),2008, 30(2): 158. [10] CHEN Yi-yi, XU Hai-song, ZHANG Xian-dou, et al(陈奕艺, 徐海松, 张显斗, 等). Acta Optica Sinica(光学学报), 2009, 29(5): 1416. |
[1] |
JI Jiang-tao1, 2, LI Peng-ge1, JIN Xin1, 2*, MA Hao1, 2, LI Ming-yong1. Study on Quantitative Detection of Tomato Seedling Robustness
in Spring Seedling Transplanting Period Based on VIS-NIR
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1741-1748. |
[2] |
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*. Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1965-1973. |
[3] |
ZHANG Shuai-shuai1, GUO Jun-hua1, LIU Hua-dong1, ZHANG Ying-li1, XIAO Xiang-guo2, LIANG Hai-feng1*. Design of Subwavelength Narrow Band Notch Filter Based on
Depth Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1393-1399. |
[4] |
JIANG Rong-chang1, 2, GU Ming-sheng2, ZHAO Qing-he1, LI Xin-ran1, SHEN Jing-xin1, 3, SU Zhong-bin1*. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1385-1392. |
[5] |
WANG Zhong, WAN Dong-dong, SHAN Chuang, LI Yue-e, ZHOU Qing-guo*. A Denoising Method Based on Back Propagation Neural Network for
Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1553-1560. |
[6] |
JI Rong-hua1, 2, ZHAO Ying-ying2, LI Min-zan2, ZHENG Li-hua2*. Research on Prediction Model of Soil Nitrogen Content Based on
Encoder-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1372-1377. |
[7] |
WANG Li-qi1, YAO Jing1, WANG Rui-ying1, CHEN Ying-shu1, LUO Shu-nian2, WANG Wei-ning2, ZHANG Yan-rong1*. Research on Detection of Soybean Meal Quality by NIR Based on
PLS-GRNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1433-1438. |
[8] |
ZHAO Yong1, HE Men-yuan1, WANG Bo-lin2, ZHAO Rong2, MENG Zong1*. Classification of Mycoplasma Pneumoniae Strains Based on
One-Dimensional Convolutional Neural Network and
Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1439-1444. |
[9] |
YAN Peng-cheng1, 2, ZHANG Chao-yin2*, SUN Quan-sheng2, SHANG Song-hang2, YIN Ni-ni1, ZHANG Xiao-fei2. LIF Technology and ELM Algorithm Power Transformer Fault Diagnosis Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1459-1464. |
[10] |
WENG Shi-zhuang*, CHU Zhao-jie, WANG Man-qin, WANG Nian. Reflectance Spectroscopy for Accurate and Fast Analysis of Saturated
Fatty Acid of Edible Oil Using Spectroscopy-Based 2D Convolution
Regression Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1490-1496. |
[11] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
[12] |
TAN Ai-ling1, CHU Zhen-yuan1, WANG Xiao-si1, ZHAO Yong2*. Detection of Pearl Powder Adulteration Based on Raman Spectroscopy and DCGAN Data Enhancement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 769-775. |
[13] |
WEN Feng-rui1, GUAN Hai-ou1*, MA Xiao-dan1, ZUO Feng2, 3*, QIAN Li-li2, 3, 4. Moldy Rice Detection Method Based on Near Infrared Spectroscopy Image Processing Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 428-433. |
[14] |
LI Yuan1, 2, SHI Yao2*, LI Shao-yuan1*, HE Ming-xing3, ZHANG Chen-mu2, LI Qiang2, LI Hui-quan2, 4. Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 490-497. |
[15] |
DENG Shi-yu1, 2, LIU Cheng-zhi1, 4*, TAN Yong3*, LIU De-long1, ZHANG Nan1, KANG Zhe1, LI Zhen-wei1, FAN Cun-bo1, 4, JIANG Chun-xu3, LÜ Zhong3. A Combination of Multiple Deep Learning Methods Applied to Small-Sample Space Objects Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 609-615. |
|
|
|
|