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

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 epsilon-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 l(1)-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. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a non-convex low-rank tensor approximation (NonLRTA) model is proposed for mixed noise removal in remote sensing hyperspectral images. An efficient augmented Lagrange multiplier (ALM) algorithm is developed to solve the proposed model. Experiments validate the superiority of the proposed method compared to state-of-the-art matrix-based and tensor-based methods.