Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization

Hyperspectral image (HSI) acquisitions are degraded by various noises, among which additive Gaussian noise may be the worst-case, as suggested by information theory. In this paper, we present a novel tensor-based HSI denoising approach by fully identifying the intrinsic structures of the clean HSI and the noise. Specifically, the HSI is first divided into local overlapping full-band patches (FBPs), then the nonlocal similar patches in each group are unfolded and stacked into a new third order tensor. As this tensor shows a stronger low-rank property than the original degraded HSI, the tensor weighted nuclear norm minimization (TWNNM) on the constructed tensor can effectively separate the low-rank clean HSI patches. In addition, a regularization strategy with spatial-spectral total variation (SSTV) is utilized to ensure the global spatial-spectral smoothness in both spatial and spectral domains. Our method is designed to model the spatial-spectral non-local self-similarity and global spatial-spectral smoothness simultaneously. Experiments conducted on simulated and real datasets show the superiority of the proposed method.