Image-denoising algorithm based on improved K-singular value decomposition and atom optimization

作     者：Rui Chen Dong Pu Ying Tong Minghu Wu 

作者机构：College of Information&Communication EngineeringNanjing Institute of TechnologyNanjingChina College of Electric Power EngineeringNanjing Institute of TechnologyNanjingChina Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage SystemHubei University of technologyWuhanChina 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2022年第7卷第1期

页      面：117-127页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by Science and Technology Research Program of Hubei Provincial Department of Education(T201805) Major Technological Innovation Projects of Hubei(No.2018AAA028) National Natural Science Foundation of China(Grant No.61703201) NSF of Jiangsu Province(BK20170765) 

主　　题：singular value smoothness 

摘      要：The traditional K-singular value decomposition(K-SVD)algorithm has poor imagedenoising performance under strong *** image-denoising algorithm is proposed based on improved K-SVD and dictionary atom ***,a correlation coefficient-matching criterion is used to obtain a sparser representation of the image *** dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the ***,non-local regularity is incorporated into the denoising model to further improve image-denoising *** of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.