A cartoon-plus-texture image decomposition model for blind deconvolution

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• 作者：Wei Wang ; Xile Zhao ; Michael Ng
• 关键词：Blind deconvolution ; Image decomposition ; Cartoon ; Texture ; Total variation ; Regularization ; Alternating minimization
• 刊名：Multidimensional Systems and Signal Processing
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：27
• 期：2
• 页码：541-562
• 全文大小：4,616 KB
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• 作者单位：Wei Wang (1)
  Xile Zhao (2)
  Michael Ng (3)
  
  1. Department of Mathematics, Tongji University, Shanghai, China
  2. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
  3. Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
  
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems
  Electronic and Computer Engineering
  Signal,Image and Speech Processing
  Artificial Intelligence and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-0824

文摘

In this paper, we study a blind deconvolution problem by using an image decomposition technique. Our idea is to make use of a cartoon-plus-texture image decomposition procedure into the deconvolution problem. Because cartoon and texture components can be represented differently in images, we can adapt suitable regularization methods to restore their components. In particular, the total variational regularization is used to describe the cartoon component, and Meyer’s G-norm is employed to model the texture component. In order to obtain the restored image automatically, we also use the generalized cross validation method efficiently and effectively to estimate their corresponding regularization parameters. Experimental results are reported to demonstrate that the visual quality of restored images by using the proposed method is very good, and is competitive with the other testing methods.