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Proceedings Paper

Study on spectral reconstruction algorithm based on kernel entropy component analysis
Author(s): Shan Sun; Xiaoxiao Zhang; Dongdong Gong; Yang Zhang; Weiping Yang
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Paper Abstract

The principal component analysis method (PCA) and the kernel entropy component analysis method (KECA) are used to construct the spectral reflectance, and study the color reproduction. . This study compares reconstruction precision through the spectral reflectance reconstruction methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and kernel entropy component analysis (KECA). Experimental results show that spectral reconstruction algorithm based on KECA is superior than PCA and KPCA in chromaticity precision and spectral precision. It has certain application value for the true color reproduction of the object surface.

Paper Details

Date Published: 5 November 2018
PDF: 11 pages
Proc. SPIE 10816, Advanced Optical Imaging Technologies, 1081615 (5 November 2018); doi: 10.1117/12.2500603
Show Author Affiliations
Shan Sun, Yunnan Normal Univ. (China)
Xiaoxiao Zhang, Yunnan Normal Univ. (China)
Dongdong Gong, Yunnan Normal Univ. (China)
Yang Zhang, Yunnan Normal Univ. (China)
Weiping Yang, Yunnan Normal Univ. (China)
Yunnan Key Lab. of Opto-electronic Information Technology (China)


Published in SPIE Proceedings Vol. 10816:
Advanced Optical Imaging Technologies
Xiao-Cong Yuan; Kebin Shi; Michael G. Somekh, Editor(s)

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