Hyperspectral Denoising Via Cross Total Variation-Regularized Unidirectional Nonlocal Low-Rank Tensor Approximation

In this paper, we propose a novel cross total variation regularized unidirectional nonlocal low rank tensor approximation method for hyperspectral image denoising. It fully explores the spectral-spatial correlation and non-local self-similarity simultaneously in tensor case and points out that the nonlocal self-similarity is the most important for precisely restoring the HSI. Following the research line in [1], we propose to embed the cross total variation (CrTV) regularization into the unidirectional low rank tensor framework to alleviate the common consistency issue of pixels in overlapped regions. CrTV shows great power to explore the spatial-spectral correlation and has great ability to keep the fine spatial details and preserve the spectra in the course of HSI denoising. The final model can be effectively solved by the alternating direction methods of multipliers (ADMM). Experimental results on HSI data sets validate that the complementary priors (i.e., spatial-spectral correlation and non local self-similarity) really contribute to the performance and also illustrate the superiority of the proposed method when compared with other state-of-the-art denoising methods.