Bi-Lp-Norm Sparsity Pursuiting Regularization for Blind Motion Deblurring

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• 关键词：Blind motion deblurring ; Bi ; Lp ; Norm ; Split Bregman method
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9948
• 期：1
• 页码：723-730
• 全文大小：979 KB
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• 作者单位：Wanlin Gan (19)
  Yue Zhou (19)
  Liming He (19)
  
  19. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
  
• 丛书名：Neural Information Processing
• ISBN：978-3-319-46672-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9948

文摘

Blind motion deblurring from a single image is essentially an ill-posed problem that requires regularization to solve. In this paper, we introduce a new type of an efficient and fast method for the estimation of the motion blur-kernel, through a bi-lp-norm regularization applied on both the sharp image and the blur kernel in the MAP framework. Without requiring any prior information of the latent image and the blur kernel, our proposed approach is able to restore high-quality images from given blurred images. Moreover a fast numerical scheme is used for alternatingly caculating the sharp image and the blur-kernel, by combining the split Bregman method and look-up table trick. Experiments on both sythesized and real images revealed that our algorithm can compete with much more sophisticated state-of-the-art methods.