Study on Spectral Reconstruction Method Based on Optimized Selected Samples

doi:10.7517/j.issn.1674-0475.2017.01.088

Abstract

Abstract:

Aiming at the defect of existing sample selection methods for spectral reflectance reconstruction, a new method based on the kernel fuzzy C-means clustering optimization sample selection is presented.This method considers the extensiveness of spectral reflectance space and the similarity of chroma space, meeting the accuracy of spectral reconstruction. Firstly, C samples are selected as the initial clustered core in the spectral reflectance space. And the original spectrais turned into(a^*,b^*) color space. At the same time, 2D chrominance space is mapped to the 3D feature space with kernel function, making characteristics linearly separable,to achieve a better result.The experiments show that this method selects training samples for spectral reflectance reconstruction can further improve the accuracy of spectral reconstruction, and it is better than the existing methods in the chroma and accuracy assessment effects.

Key words: spectroscopy, kernel fuzzy clustering, sample selection, spectral reconstruction

LONG Yanqun, WANG Huiqin, WANG Ke, WANG Zhan, ZHAO Suwen. Study on Spectral Reconstruction Method Based on Optimized Selected Samples[J]. Imaging Science and Photochemistry, 2017, 35(1): 88-96.

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