Efficient Marginal Likelihood Optimization in Blind Deconvolution
Name
MIT-CSAIL-TR-2011-020.pdf
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1.9 MB
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Adobe PDF
Checksum (MD5)
94defaa082297e6c102acdb694f4ab34
Author(s)
Levin, Anat
Weiss, Yair
Durand, Fredo
Freeman, William T.
Advisor(s)
Fredo Durand
Date Issued
April 4, 2011
Series/Report no.
MIT-CSAIL-TR-2011-020
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 MAPx,k strategies which estimate the mode pair x, k and often lead to undesired results, and MAPk strategies which select the best k while marginalizing over all possible x images. The MAPk principle is significantly more robust than the MAPx,k one, yet, it involves a challenging marginalization over latent images. As a result, MAPk techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAPk algorithm which involves only a modest modification of common MAPx,k algorithms. We show that MAPk can, in fact, be optimized easily, with no additional computational complexity.
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