基于人眼视觉和残差补偿的光谱降维模型的研究（英文）

发布时间：2018-04-07 17:09

本文选题：主成分分析　切入点：人眼视觉加权　出处：《光谱学与光谱分析》2017年05期

【摘要】：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。
[Abstract]:The traditional principal component PCA spectral dimensionality reduction method makes use of mathematical method to ensure that the reconstructed spectrum after dimensionality reduction is as similar as possible to the original spectrum, but the traditional PCA dimensionality reduction process does not discriminate the spectral data of each band.However, the sensitivity of human vision to different spectral bands is different, which sometimes results in a large color difference although the spectral error is small.In order to reduce the chrominance error of source spectrum and reconstructed spectrum effectively, two kinds of weighting functions based on human vision are proposed to optimize the traditional PCA dimensionality reduction method.The residual spectrum is used to compensate the model.In the experiment, the Munsell color card is used as the training sample and the multispectral image "young girl" is used as the test sample. Then the PCA dimension reduction and reconstruction are carried out by using the weighted function proposed in this paper, and the results are compared with the methods proposed in the relevant literature.The experimental results show that compared with other algorithms, the proposed two weighted algorithms can significantly improve the chromaticity accuracy and the stability of the variable light source.
【作者单位】：上海理工大学光电信息与计算机工程学院;河南牧业经济学院包装与印刷工程学院;上海理工大学出版印刷与艺术设计学院;河南工程学院材料与化学工程学院;
【基金】：National Natural Science Foundation Issues(61301231) the Science and Technology Development Project in USST(16KJFZ017)
【分类号】：TP391.41

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