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MRF-Based Blind Image Deconvolution

  • Nikos Komodakis
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.

Keywords

Markov Random Field Blind Deconvolution Deconvolution Algorithm Blur Kernel Image Deconvolution 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nikos Komodakis
    • 1
  • Nikos Paragios
    • 2
  1. 1.Ecole des Ponts ParisTechFrance
  2. 2.Ecole Centrale de ParisFrance

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