基于权重的鲁棒性PCA压缩方法

为了降低具有噪声的多光谱图像在降维重建后的光谱信息和颜色信息的损失,提出一种基于权重的鲁棒性PCA压缩方法(WRPCA)。先依据人眼的视觉特征,用人类视觉敏感函数(CIE1931标准观察者的色匹配函数)对多光谱图像的光谱进行加权,然后再对加权后的光谱使用Robust PCA法进行降维,最后重构得到图像。实验中WRPCA法与WSPCA法是在同样条件下进行测试。分析实验数据可以看出,WSPCA法由于受噪声影响,其图像压缩重构效果不好,但是WRPCA法不受噪声影响,其重建图像的光谱精度和色度精度都优于WSPCA法。

Research on a Robust PCA Compression Method Based on Weights

In order to reduce the loss of spectral and color information of multispectral images with noise after dimensionality reduction, a robust PCA compression method based on weights (WRPCA) was proposed. Firstly, based on the special performance of the human eye, the spectrum of the multispectral image was weighted by the human visual sensitivity function (color matching function of the CIE1931 standard observer), and then the weighted spectrum was used to reduce the image using the Robust PCA method. Finally, the image was reconstructed. In the experiment, WRPCA was tested under the same conditions as the WSPCA method. From the experimental data analysis, it was found that the WSPCA method was not good for the compression and reconstruction of the image due to the influence of noise, while the WRPCA method was not affected by the noise, and could make the reconstructed image in spectral precision and chromaticity. The accuracy and other aspects were superior to the WSPCA method. Therefore, the WRPCA method could achieve effective compression of noise-containing multispectral images and minimize color information loss.