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Stochastic Deconvolution
AbstractWe 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.
Related Material
[pdf][bibtex]@InProceedings{Gregson_2013_CVPR,
author = {Gregson, James and Heide, Felix and Hullin, Matthias B. and Rouf, Mushfiqur and Heidrich, Wolfgang},
title = {Stochastic Deconvolution},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}
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