Bayesian problems

Friday, February 10, 2006 - 10:30am - 11:30am
Michael Black (Brown University)
Bayesian denoising of archival film requires a likelihood model that
captures the image noise and a spatial prior that captures the
statistics of natural scenes. For the former we learn a statistical
model of film noise that varies as a function of image brightness. For
the latter we use the recently proposed Field-of-Experts framework to
learn a generic image prior that capture the statistics of natural
scenes. The approach extends traditional Markov Random Field (MRF)


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