Spectral Dimension Reduction Model Research Based on Human Visual Characteristics and Residual Error Compensation
LIU Shi-wei1,2, LIU Zhen3*, TIAN Quan-hui1, ZHU Ming4
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2. Department of Printing and Packaging Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou 450011，China
3. College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China
4. Department of Materials and Chemical Engineering, Henan Institute of Engineering, Zhengzhou 450007，China
Abstract：Traditional principal component analysis (PCA) keep the similar shape of original spectral reflectance and reconstructing spectral reflectance as far as possible through mathematical method. But traditional dimension-reduction algorithm of PCA calculates and processes the spectral data each wavelength with equal weighted, while the sensitivity of human vision is different at different wavelength. It would result in that the spectral errors of reconstruction are small but the color differences of reconstruction color are large by human perception. In order to control the spectral error and reduce the chromatic difference between the original spectral and reconstructed spectral, this paper presents two kinds of human-vision-weighted function to optimize the traditional PCA, and using spectral residual error to compensate dimension-reduction model. With the experiment of training samples of Munsell color, and testing samples of multispectral image (young girl) and part of Munsell color, we reduced and reconstructed the spectral color and spectral image with our proposed-function-PCA, and compared with the other methods mentioned by related articles. The experimental results indicate that the performance of our methods improve the chromatic precision and stability in the different lighting resource.
基金资助: National Natural Science Foundation Issues (61301231), the Science and Technology Development Project in USST (16KJFZ017)
通讯作者:
刘 真
E-mail: lunaprint@163.com
作者简介: LIU Shi-wei, (1975—), doctor candidate in Shanghai University of Science and Technology e-mail：
hnzzlsw@163.com
引用本文:
刘士伟，刘 真，田全慧，朱 明. 基于人眼视觉和残差补偿的光谱降维模型的研究[J]. 光谱学与光谱分析, 2017, 37(05): 1601-1605.
LIU Shi-wei, LIU Zhen, TIAN Quan-hui, ZHU Ming. Spectral Dimension Reduction Model Research Based on Human Visual Characteristics and Residual Error Compensation. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1601-1605.
[1] Lin Youwu, Deng Baichuan et al. Chemometrics and Intelligent Laboratory Systems, 2016，150(15)：58.
[2] Andreas Artemiou, Li Bing. Journal of Multivariate Analysis, 2013，119：176.
[3] Wang Yifei, Ma Xiandong, Joyce Malcolm J. Renewable Energy, 2016，97：444.
[4] He Songhua，Zhen I，Chen Qiao. Acta Optiea Siniea，2014, 34(2)：0233001-1.
[5] Flinkman M, Laamanen H, Tuomela J, et al. Optical Society of America, 2013, 30(9): 1806.
[6] Ding Guohua, Zhu Yuanhong, Li Bo, et al. Package Engineering, 2012, 33(2): 14.
[7] Tian J, Tang Y. Optics Letters, 2013, 38(15): 2818.
[8] Laamanen H, Jetsu T, Jaaskelainen T, et al. JOSA A, 2008, 25(6): 1383.
[9] Hu Weijie, Tang Shunfang. Modern Color Science and Application. Beijing：BeiJing Institute of Technology Press, 2007.
[10] Babaei V, Amirshahi S H, Agahian F. Color Research and Application, 2011, 36(4): 295.
[11] Wang Ying. A Study of Key Technologies in Multispectral Image Dolor Reproduction. Xi’an：Xidian University, 2010.
