Stochastic deconvolution

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

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


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 non-convex 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.

Original languageEnglish (US)
Article number6618983
Pages (from-to)1043-1050
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2013
Externally publishedYes
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013


  • Deblurring
  • Deconvolution
  • Random Walk
  • Spatially-Varying PSF
  • Stochastic

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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