Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising

Abstract

Hyperspectral image (HSI) enjoys great advantages over more traditional image types for various applications due to the extra knowledge available. For the nonideal optical and electronic devices, HSI is always corrupted by various noises, such as Gaussian noise, deadlines, and stripings. The global correlation across spectrum (GCS) and nonlocal self-similarity (NSS) over space are two important characteristics for HSI. In this paper, a nonlocal low-rank regularized CANDECOMP/PARAFAC (CP) tensor decomposition (NLR-CPTD) is proposed to fully utilize these two intrinsic priors. To make the rank estimation more accurate, a new manner of rank determination for the NLR-CPTD model is proposed. The intrinsic GCS and NSS priors can be efficiently explored under the low-rank regularized CPTD to avoid tensor rank estimation bias for denoising performance. Then, the proposed HSI denoising model is performed on tensors formed by nonlocal similar patches within an HSI. The alternating direction method of multipliers-based optimization technique is designed to solve the minimum problem. Compared with state-of-the-art methods, the proposed algorithm can greatly promote the denoising performance of an HSI in various quality assessments.