Hyperspectral mixed denoising via subspace low rank learning and BM4D filtering

This paper proposes a novel mixed noise removal method via subspace low rank representation and BM4D filtering for hyperspectral imagery (HSI). The proposed method is based on the following two facts. The first one is that the spectra in each class of HSI lie in different low-rank subspace, that is, the HSI data could be decomposed into two sub-matrices with lower ranks in the framework of subspace low rank representation. The second one is that the spatial structures of HSI have the property of non-local self-similarity (NSS), and the NSS could be effectively exploited by BM4D filter with no additional parameters. The proposed model can be easily and effectively solved by splitting it into several sub-problems via the alternating direction method of multipliers (ADMM). Experimental results validate that the proposed method outperforms other state-of-the-art denoising methods for HSI.