Efficient Marginal Likelihood Optimization in Blind Deconvolution

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...

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Bibliographic Details
Main Authors: Levin, Anat, Weiss, Yair, Durand, Fredo, Freeman, William T.
Other Authors: Fredo Durand
Published: 2011
Online Access:http://hdl.handle.net/1721.1/62035
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Summary: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.