易文娟,张雷洪.基于样本选择的光谱重构研究[J].包装工程,2018,39(13):233-238.
YI Wen-juan,ZHANG Lei-hong.Spectral Reconstruction Based on Sample Selection[J].Packaging Engineering,2018,39(13):233-238. |
| 基于样本选择的光谱重构研究 |
| Spectral Reconstruction Based on Sample Selection |
| 投稿时间：2018-01-09 修订日期：2018-07-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.13.037 |
| 中文关键词: 光谱反射率 主成分分析法 训练样本 重建精度 |
| 英文关键词: spectral reflectance principal component analysis (PCA) training sample reconstruction accuracy |
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| 中文摘要: |
| 目的 为了提高使用主成分分析法重构光谱反射率的重构精度。方法 利用Matlab进行仿真实验，选择3种不同色卡作为训练样本，使用主成分分析法探究主成分个数和样本间隔对重构结果的影响。结果 主成分个数为4时，贡献率均超过99%；样本间隔为10 nm时，RC24色卡重构效果最好，其平均色差2.37 平均均方根误差为0.0185。结论 训练样本的选择会影响光谱重构精度，RC24色卡具有数据量小、重建精度较高的特点，在颜色复制领域可以优先选择。 |
| 英文摘要: |
| The work aims to improve the reconstruction accuracy of spectral reflectance with principal component analysis (PCA). Matlab was used to conduct simulation experiments. 3 different color cards were selected as the training samples and the effect of the number of principal components and the sample interval on the reconstruction results was investigated by the method of PCA. When the number of principal components was 4, the contribution rate was over 99%. When the sample interval was 10 nm, the RC24 color card had the best reconstruction effect, its average color difference was 2.37 and the average root-mean-square error was 0.0185. The selection of training samples will affect the accuracy of spectral reconstruction. With the characteristics of small amount of data and high accuracy of reconstruction, RC24 color card can be preferred in the field of color reproduction. |
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