基于双层动态筛选训练样本的光谱重建算法  

Spectral Reconstruction Algorithm Based on Dual Dynamic Training Samples Selection Method

作　　者：刘士伟[1,2] 刘真[1] 田全慧[1] 张建青[1] 

机构地区：[1]上海理工大学,上海200093 [2]河南牧业经济学院,郑州450006

出　　处：《包装工程》2017年第3期160-164,共5页Packaging Engineering

基　　金：上海理工大学科技发展项目(16KJFZ017)

摘　　要：目的研究光谱重建过程中训练样本筛选方法对光谱重建精度的影响。方法利用违逆的方法对测试样本Munsell样本和Color Checker SG样本进行光谱重构,训练样本分别选择未经筛选的Munsell样本集、经过动态聚类筛选的和经过文中提出的双重动态筛选的Munsell样本集,然后比较3种样本筛选方法得到的光谱重构精度。结果实验结果表明,经过双层动态筛选的训练样本重构精度无论是均方根误差(RMSE)、拟合优度(GFC)还是不同光源下(A,D50和F2)的色差,明显高于动态聚类分析的样本和未经筛选的样本。结论提出了一种新的样本筛选方法,该筛选方法效果良好,具有一定的先进性。The work aims to study the influence of the training sample selection method in the process of spectral reconstruction on the spectral reconstruction accuracy. Munsell and Color Checker SG（test samples） were reconstructed by using the method of pseudo inverse. Training samples were selected from unscreened Munsell sets and the Munsell sets selected through dynamic clustering and dual dynamic selection proposed in the paper. Then the spectral reconstruction accuracy was obtained by comparing three sample selection methods. The experimental results showed that the reconstruction accuracy of training samples subject to double dynamic selection was apparently higher than that of the samples analyzed by dynamic clustering and the unscreened samples, whether it was root-mean-square error（RMSE）, goodness of fit（GFC） or color chromatic error under different light sources（A, D50, and F2）. A new sample selection method is proposed. The selection method brings good effects and it is advanced to some extent.

关 键 词：双层动态样本筛选 光谱重建 样本筛选 重建精度 

分 类 号：TS802.3[轻工技术与工程] TS801.3