Study on spectral reconstruction algorithm based on kernel entropy component analysis

Shan Sun; Xiaoxiao Zhang; Dongdong Gong; Yang Zhang; Weiping Yang

doi:10.1117/12.2500603

5 November 2018 Study on spectral reconstruction algorithm based on kernel entropy component analysis

Shan Sun, Xiaoxiao Zhang, Dongdong Gong, Yang Zhang, Weiping Yang

Proceedings Volume 10816, Advanced Optical Imaging Technologies; 1081615 (2018) https://doi.org/10.1117/12.2500603
Event: SPIE/COS Photonics Asia, 2018, Beijing, China

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.

Citation Download Citation

Shan Sun, Xiaoxiao Zhang, Dongdong Gong, Yang Zhang, and Weiping Yang "Study on spectral reconstruction algorithm based on kernel entropy component analysis", Proc. SPIE 10816, Advanced Optical Imaging Technologies, 1081615 (5 November 2018); https://doi.org/10.1117/12.2500603

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