| Summary: | In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of HSIs. First, the global correlations among all modes are explored by the Tucker decomposition, which applies low-rank constraints to the clean HSIs. To avoid over-smoothing, we propose GHSSTV regularization to ensure the group sparsity not only in the first-order gradient domain but also in the second-order ones along the spatio-spectral dimensions. Then, the sparse noise in HSI can be detected by the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mo>ℓ</mo><mn>1</mn></msub></semantics></math></inline-formula> norm. Furthermore, strong Gaussian noise is simulated by the Frobenius norm. The alternating direction multiplier method (ADMM) algorithm is employed to effectively solve the GHSSTV model. Finally, experimental results from a series of simulations and real-world data suggest a superior performance of the GHSSTV method in HSIs denoising.
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