Reducing Mixed Noise from Hyperspectral Images

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. Inherent structure of hyperspectral images has been exploited by utilizing 2D-total variation along spatial dimension and 1D-total variation along spectral dimension. The denoising problem has been formulated as an optimization problem whose solution has been derived using the split-Bregman approach. Experimental results demonstrates that proposed algorithm is able to reduce 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 proposed algorithm in terms of peak signal to noise ratio, structural similarity and the visual quality.