Weighted Group Sparsity Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Restoration

Total variation (TV) regularization has been widely used in the hyperspectral image (HSI) mixed noise removal problem by utilizing ℓ1-norm to constrain the spatial difference image and promote the piecewise smooth structure. Unfortunately, it cannot depict the group sparse structure of spatial difference image along the spectral dimension. This paper proposes a new HSI restoration method using weighted group sparsity regularized low-rank tensor decomposition (LRTDGS). Specifically, we use a weighted group sparsity regularization which is denoted by ℓ2,1-norm to explore the group structure of spatial difference image along the spectral dimension. Moreover, the spatial-spectral correlation from three directions of HSI is depicted by low-rank Tucker decomposition. We use efficient augmented Lagrange multiplier method to optimize the proposed LRTDGS model, and a series of experimental results are presented to demonstrate the effectiveness of the proposed method.