Hyperspectral Image Denoising Via Convex Low-Fibered-Rank Regularization

In this paper, we propose a new tensor rank, named fibered rank, by generalizing the tensor singular value decomposition (t-SVD) to the mode-k t-SVD. It factorizes a three-way tensor into two mode-k orthogonal tensors and a mode-k diagonal tensor. To efficiently remove mixed noise and finely preserve the structure information, we propose a novel hyperspectral image denoising model based on the three-directional tensor nuclear norm (3DTNN), which is a convex relaxation of the fibered rank. An efficient alternating direction method of multipliers (ADMM)-based algorithm is developed to solve the proposed model. Experimental results demonstrate the superiority of the proposed method over the compared ones.