Abstract: Hyperspectral images (HSIs) are often interfered by hybrid noise in the acquisition process,which seriously weakens the performance of subsequent applications of HSIs.In this paper,nonconvex regularizer is used to reconstruct the approximation problem instead of the traditional nuclear norm,which guarantees a tighter approximation of the original sparsity constrained rank function.Then a hybrid noise removal model integrating nonconvex surrogate function,total variation regularization and l_2,1 norms together into a unified framework is proposed.The proposed algorithm aims to decompose the degraded HSIs into low rank components and sparse terms in the matrix mode,and uses total variation regularization to maintain edge information and improve the spatial piecewise smoothness of the HSIs.Finally,using the special properties of nonconvex surrogate function,an iterative algorithm based on augmented Lagrangian multiplier method is used for optimization.Extensive experiments on several well-known datasets are conducted for model evaluation,and the results show that the proposed algorithm can not only effectively remove hybrid noise,but also can better maintain the structure and details of the images.Compared with other existing hyperspectral denoising methods,the visual effects and quantitative evaluation results of the proposed algorithm are significantly better.

Key words: Augmented lagrangian multipliers, Hybrid noise, Hyperspectral image, Nonconvex regularizer, Total variation