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A MAP-Estimation Framework for Blind Deblurring Using High-Level Edge Priors*
Yipin Zhou1 and Nikos Komodakis2
1Brown University, USA
yipin_zhou@brown.edu
2Universite Paris-Est, Ecole des Ponts ParisTech, France
nikos.komodakis@enpc.fr
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.
*Part of this work was done while the first author was an intern at Ecole des Ponts ParisTech.
LNCS 8690, p. 142 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014