Hyperspectral Restoration Employing Low Rank and 3D Total Variation Regularization

This paper presents a novel mixed-noise removal method by employing low rank constraint and 3-D total variation regularization for hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in HSI lie in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rank property of HSI is exploited by the nuclear norm, while the spectral-spatial smoothness is explored by 3D total variation (3DTV) which is defined as a combination of 2-D spatial TV and 1-D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by alternating direction method of multipliers (ADMM). Experimental results on simulated HSI dataset validate the superior performance of the proposed method.