Blind deconvolution using a normalized sparsity measure

Dilip Krishnan, Terence Tay, Robert Fergus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
Pages233-240
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, CO, United States
Duration: Jun 20 2011Jun 25 2011

Other

Other2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
CountryUnited States
CityColorado Springs, CO
Period6/20/116/25/11

Fingerprint

Deconvolution
Cost functions
Costs

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Krishnan, D., Tay, T., & Fergus, R. (2011). Blind deconvolution using a normalized sparsity measure. In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (pp. 233-240). [5995521] https://doi.org/10.1109/CVPR.2011.5995521

Blind deconvolution using a normalized sparsity measure. / Krishnan, Dilip; Tay, Terence; Fergus, Robert.

2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. 2011. p. 233-240 5995521.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Krishnan, D, Tay, T & Fergus, R 2011, Blind deconvolution using a normalized sparsity measure. in 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011., 5995521, pp. 233-240, 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, United States, 6/20/11. https://doi.org/10.1109/CVPR.2011.5995521
Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure. In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. 2011. p. 233-240. 5995521 https://doi.org/10.1109/CVPR.2011.5995521
Krishnan, Dilip ; Tay, Terence ; Fergus, Robert. / Blind deconvolution using a normalized sparsity measure. 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. 2011. pp. 233-240
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