Unidirectional Spatial and Spectral Smoothed Tensor Ring Decomposition for Hyperspectral Image Denoising and Destriping.

In this letter, we propose a novel unidirectional spatial and spectral smoothed tensor ring decomposition (U3STR) for hyperspectral image (HSI) denoising and destriping. The powerful tensor ring (TR) decomposition is introduced to explore the global spatial-spectral correlation of HSI, which transforms the restoration of HSI into estimating three TR factors. To address the local spatial-spectral smoothness of HSI and the directional characteristic of stripe noise, unidirectional spatial and spectral smoothed constraints are applied to the horizontal spatial and spectral TR factors, respectively. Moreover, considering the stripe noise shares spatial correlation and local smoothness with the image component, we strategically utilize band-by-band low-rank and unidirectional total variation (TV) regularization, effectively disentangling stripe noise from the image content without conflicting the image regularization. The proposed U3STR model is solved by the alternating direction method of multipliers (ADMM) algorithm effectively. Experimental results demonstrate that our method outperforms other HSI restoration methods in denoising and destriping, notably enhancing the quality of the restored image by an average of 3 dB over existing methods.