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A MAP-Estimation Framework for Blind Deblurring Using High-Level Edge Priors

  • Yipin Zhou
  • Nikos Komodakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

In this paper we propose a general MAP-estimation framework for blind image deconvolution that allows the incorporation of powerful priors regarding predicting the edges of the latent image, which is known to be a crucial factor for the success of blind deblurring. This is achieved in a principled, robust and unified manner through the use of a global energy function that can take into account multiple constraints. Based on this framework, we show how to successfully make use of a particular prior of this type that is quite strong and also applicable to a wide variety of cases. It relates to the strong structural regularity that is exhibited by many scenes, and which affects the location and distribution of the corresponding image edges. We validate the excellent performance of our approach through an extensive set of experimental results and comparisons to the state-of-the-art.

Keywords

Latent Image Image Edge Edge Pixel Blind Deconvolution Blur Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yipin Zhou
    • 1
  • Nikos Komodakis
    • 2
  1. 1.Brown UniversityUSA
  2. 2.Ecole des Ponts ParisTechUniversite Paris-EstFrance

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