A new reweighted minimization algorithm for image deblurring

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• 作者：Tiantian Qiao (5) (6)
  Boying Wu (6)
  Weiguo Li (5)
  Alun Dong (5)
  
  5. College of Science ; China University of Petroleum ; Changjiangxi Road 66 ; Qingdao ; 266580 ; P.R. China
  6. Department of Mathematics ; Harbin Institute of Technology ; West Dazhi Street 92 ; Haerbin ; 150001 ; P.R. China
  
• 关键词：reweighted minimization ; generalized inverse ; linearized Bregman iteration ; image deblurring
• 刊名：Journal of Inequalities and Applications
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：2014
• 期：1
• 全文大小：359 KB
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• 刊物主题：Analysis; Applications of Mathematics; Mathematics, general;
• 出版者：Springer International Publishing
• ISSN：1029-242X

文摘

In this paper, a new reweighted minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted minimization problem . In the computing process, the effective using of signal information can make up the detailed features of image, which may be lost in the deblurring process. Numerical experiments confirm that the new reweighted algorithm for image restoration is effective and competitive to the recent state-of-the-art algorithms.