Gradient-guided sparse representation for hyperspectral image denoising

In this paper, a gradient-guided sparse representation method (GGSR) for the hyperspectral image denoising is proposed. In the context of the hyperspectral image, neighbourhood spectral bands always have highly similar spatial and structural characteristics, which can be jointly used to improve the image quality. On the one hand, the sparse representation, as one powerful image processing tool, is introduced to jointly sparsely code similar image patches from different spectral bands. By this way, the redundant spatial similarity can be effectively exploited. On the other hand, the reference gradient is incorporated with the sparse representation model, in order to exploit the redundant structural information to better preserve the structure/texture characteristics. Practically, the gradient reference can be estimated from the neighbouring structural similar spectral bands. Experimental results demonstrate the effectiveness of the proposed method in removing noise as well as preserving structures.