Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

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• 作者：Daniele Perrone (19)
  Remo Diethelm (19)
  Paolo Favaro (19)
  
• 关键词：blind deconvolution ; majorization ; minimization ; primal ; dual ; image prior
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8932
• 期：1
• 页码：112-125
• 全文大小：1,517 KB
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• 作者单位：Daniele Perrone (19)
  Remo Diethelm (19)
  Paolo Favaro (19)
  
  19. Department of Computer Science and Applied Mathematics, University of Bern, Neubr眉ckstrasse 10, 3012, Bern, Switzerland
  
• 丛书名：Energy Minimization Methods in Computer Vision and Pattern Recognition
• ISBN：978-3-319-14612-6
• 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

文摘

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.