Study on Spectral Reconstruction Algorithm Based on Kernel Entropy Component Analysis

ADVANCED OPTICAL IMAGING TECHNOLOGIES(2018)

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摘要
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.
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关键词
Multi-spectral imaging,Spectral reflectance reconstruction,Principal component analysis,Kernel principal component analysis,Kernel entropy component analysis
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