Stochastic Deconvolution

James Gregson, Felix Heide, Matthias B. Hullin, Mushfiqur Rouf, Wolfgang Heidrich

CVPR 2013

doi:10.1109/CVPR.2013.139 /cvpr/2013/gregson2013cvpr-stochastic/

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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Text

Gregson et al. "Stochastic Deconvolution." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.139

Markdown

[Gregson et al. "Stochastic Deconvolution." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/gregson2013cvpr-stochastic/) doi:10.1109/CVPR.2013.139

BibTeX

@inproceedings{gregson2013cvpr-stochastic,
  title     = {{Stochastic Deconvolution}},
  author    = {Gregson, James and Heide, Felix and Hullin, Matthias B. and Rouf, Mushfiqur and Heidrich, Wolfgang},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.139},
  url       = {https://mlanthology.org/cvpr/2013/gregson2013cvpr-stochastic/}
}