Revisiting loss-specific training of filter-based MRFs for image restoration

16 Jan 2014Yunjin ChenThomas PockRené RanftlHorst Bischof

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate... (read more)

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