Group-based hyperspectral image denoising using low rank representation

For the hyperspectral image (HSI) denoising problem, we propose a group-based low rank representation (GLRR) method. A corrupted HSI is divided into overlapping patches and the similar patches are combined into a group. The group is de-noised as a whole using low rank representation (LRR). Our method can employ both the local similarity within the patch and the nonlocal similarity across the patches within a group simultaneously, while nonlocal similar patches within the group can bring extra structure information for the corrupted patch, which makes the noise more significant to be detected as outliers. Since the uncorrupted patches have an intrinsic low-rank structure, LRR is employed for the denoising of the patch group. Both simulated and real data are used in the experiments. The effectiveness of our method is proved both qualitatively and quantitatively.