Hyperspectral Image Denoising via Nonlocal Rank Residual Modeling

In this paper, we propose a novel nonlocal rank residual (NRR) approach for highly effective HSI denoising, which progressively approximates the underlying LR tensor via minimizing the rank residual. Towards this end, we first obtain a good estimate of the original nonlocal full-band group by using the NSS prior, and then the rank residual between the degraded nonlocal full-band group with the corresponding estimated nonlocal full-band group is minimized to achieve a more accurate LR tensor. Moreover, the global spectral LR prior is employed to reduce the spectral redundancy of HSI in the proposed denoising framework. Finally, we develop a simple yet effective alternating minimization algorithm to jointly refine global spectral information and nonlocal full-band groups. Experimental results clearly show that the proposed NRR algorithm outperforms many state-of-the-art HSI denoising methods.