Noise reduction for hyperspectral images based on structural sparse and low-rank matrix decomposition

In this paper, a noise reduction approach for hyperspectral images (HSIs) is presented. Due to the assorted noise sources of HSIs, it seems difficult to describe the noise in a concise manner. Commonly, noise reduction algorithms are dedicated to a certain kind of noise, such as random or striping noise. Most of them in addition have somewhat idealized hypotheses. For example, the random noise is white or signal-independent, or the observed scene is spatially homogeneous or quasi-homogeneous. Thus a practically efficient and universal denoising method is preferred. Thanks to the low-rank characteristic of HSI signal, and the structural sparsity of HSI noise, we draw inspiration from low-rank matrix decomposition and the emerging mixed norm, to propose a method dealing with various patterns of noise simultaneously. Both simulated and real data experiments show the effectiveness of the proposed approach.