面向高保真再现的多光谱图像降维方法

目的研究满足面向高保真再现要求的多光谱图像降维方法。方法基于二进制小波对信号的分解与人类的视觉特性相匹配,以及非负主成分分析法可较好地保证降维的光谱精度,提出采用基于离散二进制小波变化与非负主成分分析法的综合降维方法,并基于多光谱图像高保真再现的光谱精度、色度精度与变光源色差稳定性的要求,提出采用CIELAB的标准色差ab?E、光谱保真度和平均梯度等3个指标来评价降维效果。结果经过多光谱图像的测试实验,基于离散小波变换和非负主成分分析法的综合降维方法相对于其他3种方法,其光谱精度、色度精度和图像清晰度保持良好。结论该方法较好地实现了多光谱图像的高保真再现问题,并且为颜色视觉的认知过程提供了新的理论解释。

Multispectral Image Dimensionality Deduction Method for High-fidelity Reproduction

The current major multispectral image dimensionality reduction methods (principal component analysis, LabPQR, WSPCAplus) cannot meet the need of the multispectral image high-fidelity reproduction. This paper researched a multispectral image dimensionality deduction method for high-fidelity reproduction. Based on the facts that binary wavelet decomposition of the signal matches the human vision characteristics and the nonnegative principal component analysis method can better ensure the spectral accuracy of the dimension reduction image, a composite dimensionality reduction method based on the discrete binary wavelet change and the nonnegative principal component analysis was put forward. Based on the spectral accuracy, chroma precision and chromatic aberration stability of changing light source, the standard color difference of CIELAB, the spectral fidelity and the image average gradient were proposed to evaluate the dimensionality reduction efficacy. After the multispectral image test, the composite dimensionality reduction method based on the discrete wavelet transform and nonnegative principal component analysis could better ensure the spectral accuracy, chroma precision and image definition when compared with the other three methods. This method could better realize the multispectral image high-fidelity production, besides providing a new theoretical explanation for the color vision cognitive process.