Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

This paper proposes a technique for reducing impulse noise from corrupted hyperspectral images. We exploit the spatiospectral correlation present in hyperspectral images to sparsify the datacube. Since impulse noise is sparse, denoising is framed as an L1-norm regularized L1-norm data fidelity minimization problem. We derive an efficient split Bregman based algorithm to solve the same. Experiments on real datasets show that our proposed technique yields better results than state-of-the-art denoising algorithms compared against. keywords Impulse noise, Total variation, Split-Bregman