Stochastic deconvolution

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

doi:10.1109/CVPR.2013.139

Stochastic deconvolution

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

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

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 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 language	English (US)
Article number	6618983
Pages (from-to)	1043-1050
Number of pages	8
Journal	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs	https://doi.org/10.1109/CVPR.2013.139
State	Published - 2013
Externally published	Yes
Event	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: Jun 23 2013 → Jun 28 2013

Keywords

Deblurring
Deconvolution
Random Walk
Spatially-Varying PSF
Stochastic

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

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10.1109/CVPR.2013.139

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title = "Stochastic deconvolution",

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 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.",

keywords = "Deblurring, Deconvolution, Random Walk, Spatially-Varying PSF, Stochastic",

author = "James Gregson and Felix Heide and Hullin, {Matthias B.} and Mushfiqur Rouf and Wolfgang Heidrich",

year = "2013",

doi = "10.1109/CVPR.2013.139",

language = "English (US)",

pages = "1043--1050",

journal = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

issn = "1063-6919",

publisher = "IEEE Computer Society",

note = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 ; Conference date: 23-06-2013 Through 28-06-2013",

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TY - JOUR

T1 - Stochastic deconvolution

AU - Gregson, James

AU - Heide, Felix

AU - Hullin, Matthias B.

AU - Rouf, Mushfiqur

AU - Heidrich, Wolfgang

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Deblurring

KW - Deconvolution

KW - Random Walk

KW - Spatially-Varying PSF

KW - Stochastic

UR - http://www.scopus.com/inward/record.url?scp=84887338032&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2013.139

DO - 10.1109/CVPR.2013.139

M3 - Conference article

AN - SCOPUS:84887338032

SN - 1063-6919

SP - 1043

EP - 1050

JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

M1 - 6618983

T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013

Y2 - 23 June 2013 through 28 June 2013

ER -

Stochastic deconvolution

Abstract

Keywords

ASJC Scopus subject areas

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