cvpr cvpr2013 cvpr2013-412 cvpr2013-412-reference knowledge-graph by maker-knowledge-mining
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Author: James Gregson, Felix Heide, Matthias B. Hullin, Mushfiqur Rouf, Wolfgang Heidrich
Abstract: We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including nonconvex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.
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