Abstract: Hyperspectral image denoising is a fundamental problem in remote sensing field,which is an important step of preprocessing.Denoising method based on total variation of representative coefficients is widely used in hyperspectral image(HSI) denoising.Representative coefficient matrix U inherits prior information of clean HSI,which can achieve global low rank and reduce computational complexity.However,due to the introduction of first-order total variational,this method produces a strong step effect in the process of denoising and ignores the common features between different bands,so the denoising effect is poor.To solve this problem,a new regularized denoising model of joint group sparse and representative coefficient bidirectional spatial spectral total variational(RCBGS) is proposed.By introducing high-order total variational,the step effect is alleviated,and the weighted $\ell$_2,1 norm is introduced into the difference of subspace to fully explore the common features of different bands except global low rank,and improve the intrinsic group sparsity and overall smoothness of HSI.Finally,the iterative rules of the proposed method are given by alternate direction multiplier method(ADMM),and the evaluation index peak signal-to-noise ratio of the proposed method is improved by 8.79% on average compared with the comparison methods.Experiments on simulated and real datasets show that the proposed method outperforms relative methods in both visual quality and quantitative evaluation.

Key words: Hyperspectral image denoising, Bidirectional variation, Low-rank prior, Staircase effect, Group sparse