Efficient marginal likelihood optimization in blind deconvolution

Anat Levin*, Yair Weiss, Fredo Durand, William T. Freeman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

583 Scopus citations

Abstract

In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAP x, k strategies which estimate the mode pair x, k and often lead to undesired results, and MAP k strategies which select the best k while marginalizing over all possible x images. The MAP k principle is significantly more robust than the MAP x, k one, yet, it involves a challenging marginalization over latent images. As a result, MAP k techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAP k algorithm which involves only a modest modification of common MAP x, k algorithms. We show that MAP k can, in fact, be optimized easily, with no additional computational complexity.

Original languageAmerican English
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages2657-2664
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Fingerprint

Dive into the research topics of 'Efficient marginal likelihood optimization in blind deconvolution'. Together they form a unique fingerprint.

Cite this