Hyperspectral image denoising via sparsity and low rank

Hyperspectral noise is unavoidable in capture and transmission process, and it will degrade the detection and classification performance greatly. Noise free signal can be approximated using few atom or basis, while noisy signal is not. There are lots of similar spatial-spectral structures in noise free hyperspectral image. On the other hand, hyperspectral image of different bands are highly correlated, the rank of hyperspectral data should be low. Based on these ideas, in this paper, we propose a hyperspectral denoising method in sparse representation framework with low rank and nonlocal regulation. Numerical experiment demonstrates that proposed denoising result is competitive with the state of art algorithm.