基于人眼视觉特性的光谱降维模型研究

针对传统光谱降维方法其降维重构后的光谱数据仅是对原始光谱的数学逼近，会出现光谱误差较小但颜色色差较大的缺点，创新性的提出三种将人眼视觉特性与光谱降维相结合的方法。其中，VPCA法直接将光谱光视效率函数加权到原始光谱上再进行降维，LMSPCA方法用LMS视稚响应构建加权矩阵对原始光谱加权后再进行降维，在LMSPCA法中加权矩阵的构建有两种方式，其主要区别在于视稚响应偏差的求取方式不同。方式一中，L，M，S视稚响应偏差是各对应波长上的偏差取绝对值，而方式二中，其偏差是各对应波长上的偏差平方。LMSPCAs法在LMSPCA法基础上再采用PCA(主成分分析)方法对损失的光谱进行降维。实验结果表明VPCA法降维效果较差，LMSPCA法的两种加权矩阵降维效果接近，皆可显著提高降维模型的色度精度，但会降低模型的光谱精度，LMSPCAs法由于针对LMSPCA法因光谱加权引起的光谱损失再进行光谱补偿，其在光谱精度、色度精度以及变光照条件下的色差稳定性这三个方面都能较好地表征原始高维光谱反射率，满足光谱颜色复制的要求。

The Research of Spectral Dimension Reduction Method Based on Human Visual Characteristics

The traditional spectral dimension reduction methods are usually carried out by matching the reconstructed spectra to the original spectra mathematically, which will often result in reconstructed spectra of small spectral reconstruction errors but very poor colorimetric accuracy when compared with the original one. In order to minimize both the spectral and colorimetric errors more efficiently, we proposed three spectral dimension reduction methods by introducing the characteristics of human vision. The first method is VPCA, in which we apply spectral luminous efficiency function to the original spectra before reduction; The Second method (LMSPCA) uses a matrix derived from LMS cone sensitivity to weight the original spectra before reduction, and the matrix can be form by two methods, in which the L, M, S cones response offset is calculated by in two different ways: one is computed as the absolute value of each corresponding wave length offset, and the other is calculated as the square of each corresponding wave length offset. The third method is LMSPCAs, which is based on the second method LMSPCA by further applying PCA to the residual spectra. The result shows that the VPCA method produces the poorest perfomance. The two cones response weighted matrixes of LMSPCA method have similar performances by presenting better colorimetric accuracy and low spectral accuracy, while LMSPCAs method which compensates for the spectral loss of LMSPCA method can produce higher spectral and colorimetric reconstruction accuracy and color stability under different light source, and satisfies the requirements of spectral color reproduction.