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中文摘要：
高光谱图像光谱分辨率的提高带来数据量的显著增加，普通针对二维图像的去噪方法不能有效地应用到高光谱图像上。根据高光谱图像本身和噪声的特征，研究基于PCA的非局部去噪方法，充分利用光谱谱间相似性和谱内相似性，首先进行PCA降维选择具有代表性的维度，然后在这些维度运用非局部的BM3D方法去除噪声，最后再返回到原图像得到去噪结果。实验结果表明，该方法的去噪效果令人满意。
英文摘要：
The improvement of spectral resolution for hyperspectral images brought significant increase of data size,but the common denoising methods for two-dimensional image cannot be effectively applied to hyperspectral image. According to the characteristics of the hyperspectral image and its noise,a non-local denoising method based on principal component analysis （PCA） is studied to make full use of the similarity between the spectra and within the spectrum. Firstly,the representative di-mension is selected by reducing the dimension of PCA,and then noise is removed by the non-local BM3D method in the repre-sentative dimension. Finally,the denoising result is obtained by returning to the original image. The experiment results show that the effect of the proposed method is satisfactory.
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