Abstract
Sparse coding has achieved great success m various image restoration tasks.However,if the sparse representation coefficients of the structure(low-frequency information)and texture(high-frequency information)components of the image are under the same penalty constraint,the restoration effect may not be ideal.In this paper,an image denoising model combining mixed norm and weighted nuclear norm as regularization terms is proposed.The proposed model simultaneously exploits the group sparsity of the high frequency and low-rankness of the low frequency in dictionary-domain.The mixed norm is used to constrain the high frequency part and the weighted nuclear norm is used to constrain the low frequency part.In order to make the proposed model easy to solve under the framework of alternative di-rection multiplier method(ADMM),iterative shrinkage threshold method and weighted nuclear norm minimization method are used to solve the two sub-problems.The validity of the model is verified experimentally through comparison with some state-of-the-art methods.
基金项目
National Natural Science Foundation of China(61701004)
Outstanding Young Talents Support Program of Anhui Province(GXYQ2021178)
NETL
NSTL
维普
万方数据