IMAGE RESTOR ATION UNDER CAUCHY NOISE WITH SPA RSE REPR ESENTATION PRIOR AND TOTAL GENER ALIZED VA RIATION  

作　　者：Miyoun Jung Myungjoo Kang 

机构地区：[1]Department of Mathematics,Hankuk University of Foreign Studies,Yongin,Korea [2]Department of Mathematical Sciences,Seoul National University,Seoul,Korea

出　　处：《Journal of Computational Mathematics》2021年第1期81-107,共27页计算数学（英文）

基　　金：Miyoun Jung was supported by Hankuk University of Foreign Studies Research Fund and the NRF(2017R1A2B1005363);Myungjoo Kang was supported by the NRF(2015R1A15A1009350,2017R1A2A1A17069644).

摘　　要：This article introduces a novel variational model for restoring images degraded by Cauchy noise and/or blurring.The model integrates a nonconvex data-fidelity term with two regularization terms,a sparse representation prior over dictionary learning and total generalized variation(TGV)regularization.The sparse representation prior exploiting patch information enables the preservation of fine features and textural patterns,while adequately denoising in homogeneous regions and contributing natural visual quality.TGV regularization further assists in effectively denoising in smooth regions while retaining edges.By adopting the penalty method and an alternating minimization approach,we present an efficient iterative algorithm to solve the proposed model.Numerical results establish the superiority of the proposed model over other existing models in regard to visual quality and certain image quality assessments.

关 键 词：Image restoration Cauchy noise Sparse representation prior Dictionary learn-ing Total generalized variation. 

分 类 号：O17[理学—数学]