A novel non-convex low-rank tensor approximation model for hyperspectral image restoration

Abstract

Remote sensing hyperspectral images (HSIs) are inevitably corrupted by several types of noise in the process of acquisition and transmission. In this paper, we propose a non-convex low-rank tensor approximation (NonLRTA) model for mixed noise removal, which can estimate the intrinsic structure of the underlying HSI from its noisy observation. The clean HSI component is characterized by the ϵ-norm, which is a non-convex surrogate to Tucker rank. The mixed noise is modeled as the sum of sparse and Gaussian components, which are regularized by the l1-norm and the Frobenius norm, respectively. An efficient augmented Lagrange multiplier (ALM) algorithm is developed to solve the proposed model. Experiments implemented on simulated and real HSIs validate the superiority of the proposed method, as compared to the state-of-the-art matrix-based and tensor-based methods.