| 摘要: |
| 目的 研究光谱反射率重建过程中训练样本的选取，以提高光谱反射率的重建精度。方法 根据检验样本与训练样本间的欧氏距离判断两者的相似度，并将样本向量各维度分量“标准化”到均值、方差相等，使得各维度分别满足标准正态分布，同时将其方差的倒数作为权重赋予训练样本。实验以Munsell色卡为总的训练样本集，经Mohammadi方法、Cao方法以及文中方法选择出的样本作为最终的训练样本，用Color Rendition Chart 24色卡为检验样本，采用伪逆法分别对选择出来的训练样本进行光谱反射率重建。结果 通过Matlab软件仿真实验，文中方法的平均色差为0.7918 ，最大色差为1.7148 ，平均均方根误差为0.0060，最大光谱均方根误差为0.0127。结论 基于加权欧氏距离的训练样本选择可以有效地提高光谱的重建精度，能更好地实现颜色再现。 |
| 关键词: 样本选择 加权欧氏距离 光谱反射率重建 |
| DOI：10.19554/j.cnki.1001-3563.2020.15.038 |
| 分类号:TS801.3 |
| 基金项目:上海理工大学科技发展项目（2018KJFZ023）；绿色制版与柔印标准化实验室资助项目（LGPSFP-03） |
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| Spectral Reconstruction Training Sample Selection Based on Weighted Euclidean Distance |
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REN Ao1, KONG Ling-jun2, LIU Zhen1, WANG Qian1
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1.Shanghai University of Science and Technology, Shanghai 200093, China;2.Shanghai Publishing and Printing College, Shanghai 200093, China
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| Abstract: |
| The work aims to study the selection of training samples for spectral reflectance reconstruction to improve the reconstruction accuracy of spectral reflectance. The similarity between the test sample and the training sample was determined according to their Euclidean distance and the dimensional components of the sample vector were ''normalized'' to the average and the variance to be equal, so that the dimensions respectively met the standard normal distribution, and the reciprocal of the variance was given to the training samples as the weight. In the experiment, the Munsell color card was used as the total training sample set, the samples selected by Mohammadi method, Cao et al method and the method presented hereinwere used as the final training samples, and Color Rendition Chart 24 was used as the test samples. Spectral reflectance reconstruction was performed on the training samples respectively selected by pseudo-inverse method. Through the Matlab software simulation experiment, the average color difference of the proposed method was 0.7918 , the maximum color difference was 1.7148 , the average root-mean-square error was 0.0060, and the maximum spectral root-mean-square error was 0.0127. The selection of training samples based on weighted Euclidean distance can effectively improve the reconstruction accuracy of the spectrum and achieve better color reproduction. |
| Key words: sample selection weighted Euclidean distance spectral reflectance reconstruction |
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