基于人眼视觉和残差补偿的光谱降维模型的研究

传统主成分(PCA)光谱降维方法利用数学的方法，保证降维后的重构光谱与原光谱在形状上尽可能相似, 但是传统PCA降维过程中无差别的对待每一个波段的光谱数据，而人眼视觉对不同波段的光谱敏感程度不同，会造成有时候虽然光谱误差较小，但是人眼看上去色差较大的情况。在保证光谱误差的同时，为了能够有效的减少源光谱与重构光谱的色度误差，提出了两种基于人眼视觉的加权函数对传统PCA降维方法进行优化，并利用残差光谱对模型进行补偿。实验过程以Munsell色卡作为训练样本，Munsell色卡和多光谱图像“young girl”作为测试样本，然后利用本文提出的加权函数进行PCA降维并重构，并与相关文献提出的方法进行了对比。实验结果表明, 提出的两种加权算法，与其他算法相比，无论是色度精度还是在变光源的稳定性方面，都有显著地提高。

Spectral Dimension Reduction Model Research Based on Human VisualCharacteristics and Residual Error Compensation

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.