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Hyperspectral images (HSIs) are inevitably degraded by a mixture of varioustypes of noise, such as Gaussian noise, impulse noise, stripe noise, and deadpixels, which greatly limits the subsequent applications. Although variousdenoising methods have already been developed, accurately recovering thespatial-spectral structure of HSIs remains a challenging problem to beaddressed. Furthermore, serious stripe noise, which is common in real HSIs, isstill not fully separated by the previous models. In this paper, we propose anadaptive hyperLaplacian regularized low-rank tensor decomposition (LRTDAHL)method for HSI denoising and destriping. On the one hand, the stripe noise isseparately modeled by the tensor decomposition, which can effectively encodethe spatial-spectral correlation of the stripe noise. On the other hand,adaptive hyper-Laplacian spatial-spectral regularization is introduced torepresent the distribution structure of different HSI gradient data byadaptively estimating the optimal hyper-Laplacian parameter, which can reducethe spatial information loss and over-smoothing caused by the previous totalvariation regularization. The proposed model is solved using the alternatingdirection method of multipliers (ADMM) algorithm. Extensive simulation andreal-data experiments all demonstrate the effectiveness and superiority of theproposed method.

Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping