Multimode Structural Nonconvex Tensor Low-Rank Regularized Hyperspectral Image Destriping and Denoising

In this letter, we propose an effective multimode structural nonconvex tensor low-rank (M2SNTLR) regularized hyperspectral image (HSI) destriping and denoising method in a unified framework of tensor representation. First, we exploit and model the tensor low-tubal-rank properties of the HSI and its spectral gradient along the spectral mode simultaneously via the tensor tubal rank functions. Second, based on the speciality and directionality of stripe noise, we particularly exploit and model the tensor low-tubal-rank properties of stripe noise and its unidirectional vertical gradient along the spatial vertical mode simultaneously also via the tensor tubal rank functions. Third, using the log tensor nuclear norm (logTNN)-based nonconvex surrogate on those tensor tubal rank functions for a closer approximation, we thus propose the logTNN-based M2SNTLR priors of HSI and stripe noise. Furthermore, we solve the proposed M2SNTLR model via the alternating direction method of multipliers (ADMM). Finally, extensive experiments validate that the proposed M2SNTLR method performs better denoising results than various low-rank-based HSI denoising methods.