Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition

To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain spectral residual total variation (SSRTV). Considering that there is much residual texture information in spectral variation image, SSRTV first calculates the difference between the pixel values of adjacent bands and then calculates a 2DTV for the residual image. Experimental results demonstrated that the SSRTV regularization term is powerful at changing the structures of noises in an original HSI, thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. The global low-rankness and spatial-spectral correlation of HSI is exploited by low-rank Tucker decomposition (LRTD). Moreover, it was demonstrated that the l(2,1) norm is more effective to deal with sparse noise, especially the sample-specific noise such as stripes or deadlines. The augmented Lagrange multiplier (ALM) algorithm was adopted to solve the proposed model. Finally, experimental results with simulated and real data illustrated the validity of the proposed method. The proposed method outperformed state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods in terms of quantitative metrics and visual inspection.