Hyperspectral Image Denoising and Destriping via Gradient Tensor Subspace Low-Rank Learning and Along-Across Stripe Directional Constraints

This article proposes a new hyperspectral image (HSI) denoising and destriping method via gradient tensor subspace low-rank learning and along-across stripe directional constraints (GTSL2A2SDCs) under the unified framework of tensor representation modeling. On one hand, for the modeling of stripe noise, based on the inherently directional and structural attributes of the stripe noise, we mainly investigate the mode-1 gradient tensor of stripe along the stripe direction which holds the preferable “zero plane” constraint as well as the mode-2 gradient tensor of stripe across the stripe direction which holds the preferable tensor low-fibered-rank attribute along the mode-3 spectral dimension. Therefore, we novelly propose the unified along-across stripe directional gradient tensor constraints for the stripe noise, which can simultaneously characterize the directional and structural attributes of the stripe noise. On the other hand, for the modeling of HSI, based on the preferably spectral low-rankness attributes of the multimode gradient tensors of HSI, namely, mode-1 gradient tensor, mode-2 gradient tensor, and mode-3 gradient tensor, we further utilize the spectral low-rank factorization of the gradient tensors of HSI to get the corresponding representation tensors, and particularly investigate the nonlocal self-similarities (NSSs)-based low-rankness of the representation tensors under the gradient tensor-based subspace low-rank learning framework. Moreover, we optimize the proposed GTSL2A2SDC model via an efficiently alternative and iterative algorithm. At last, extensive experiments comprehensively validate the denoising and destriping capacity and superiority of GTSL2A2SDC.