Hyperspectral Image Destriping and Denoising Via Stripe Unidirectional Gradient Low Rank and Non-convex Tensor Low-Tubal-Rank Priors

In this article, a new hyperspectral image (HSI) destriping and denoising method via stripe unidirectional gradient low rank and non-convex tensor low-tubal-rank priors is proposed. Concretely, we propose the log tensor nuclear norm-based non-convex tensor low-tubal-rank prior to model the tensor low-tubal-rank property of HSI. Furthermore, we particularly propose the nuclear norm-based low rank prior of the unidirectional vertical gradient of stripe noise. Then, the alternating direction method of multipliers (ADMM) is used to solve the proposed model. Experimental results have demonstrated that the proposed method significantly outperforms various low rank-based HSI denoising methods.