Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation

TL;DR

This paper introduces a novel hyperspectral image denoising method combining global spatial-spectral total variation with nonconvex local low-rank tensor approximation, effectively removing mixed noise while preserving details.

Contribution

It proposes a new tensor $L_{b3}$-norm for local low-rank prior and integrates SSTV regularization to enhance denoising performance in HSIs.

Findings

Improved preservation of local details and structural information.

Effective removal of mixed noise in simulated and real datasets.

Outperforms traditional methods in denoising quality.

Abstract

Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one. Noise contamination can often be caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the clean HSI data have its underlying local LR tensor property, even though the real HSI data may not be globally low-rank due to out-liers and non-Gaussian noise. According to this fact, we propose a novel tensor $L_{\gamma}$-norm to formulate the local LR prior. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial and spectral domains. Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial structure and spectral correlation of neighboring bands. Results on simulated and real HSI datasets indicate that the use of local LR tensor penalty and global SSTV can boost the preserving of local details and overall structural information in HSIs.