Hyperspectral Image Denoising Using Spatio-Spectral Total Variation

Abstract

This letter introduces a hyperspectral denoising algorithm based on spatio-spectral total variation. The denoising problem has been formulated as a mixed noise reduction problem. A general noise model has been considered which accounts for not only Gaussian noise but also sparse noise. The inherent structure of hyperspectral images has been exploited by utilizing 2-D total variation along the spatial dimension and 1-D total variation along the spectral dimension. The denoising problem has been formulated as an optimization problem whose solution has been derived using the split-Bregman approach. Experimental results demonstrate that the proposed algorithm is able to reduce a significant amount of noise from real noisy hyperspectral images. The proposed algorithm has been compared with existing state-of-the-art approaches. The quantitative and qualitative results demonstrate the superiority of the proposed algorithm in terms of peak signal-to-noise ratio, structural similarity, and the visual quality.