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Understanding image priors in blind deconvolution
Open accessAbstract
Removing blurs from a single degraded image without any knowledge of the blur kernel is an ill-posed blind deconvolution problem. Proper estimators together with correct image priors play a fundamental role in accurate blind de-convolution. We demonstrate a superior performance of the variational Bayesian estimator and discuss suitability of automatic relevance determination distributions as image priors. Restoration of real photos blurred by out-of-focus and motion blur, and comparison with a state-of-the-art method is provided.
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“derives effective results with heuristic techniques”
From A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring (2020) · cites this paper as [28]
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“One popular method of solving this kind problem is by using priors on the image.”
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- 10.1109/TPAMI.2011.148 · DOI
- miskin, 2000, Ensemble learning for blind image separation and deconvolution, Advances in Independent Component Analysis, 123, 10.1007/978-1-4471-0443-8_7 · DOI
- 10.1109/83.869189 · DOI
- wipf, 2008, A new view of automatic relevance determination, NIPS 20
- derin babacan, 2012, Bayesian blind deconvolution with general sparse image priors, ECCV, 7577, 341
- 10.1109/TIP.2008.2011757 · DOI · Paper page
- aubert, 2002, Mathematical Problems in Image Processing, 10.1007/b97428 · DOI
- 10.1109/TIP.2009.2031231 · DOI
- 10.1109/34.120331 · DOI
- 10.1109/CVPR.2009.5206815 · DOI · Paper page
- rob, 2006, Removing camera shake from a single photograph, SIGGRAPH '06 ACM SIGGRAPH 2006 Papers, 787
- 10.1109/83.661187 · DOI
- 10.1109/CVPR.2007.383029 · DOI
- 10.1109/TIP.2006.881972 · DOI
- 10.1145/1399504.1360672 · DOI
- 10.1109/CVPR.2008.4587834 · DOI
- 10.1364/OL.13.000547 · DOI
- 10.1109/TCOM.1980.1094608 · DOI
- xu, 0, Two-phase kernel estimation for robust motion deblurring, Proceedings of the 11 th European conference on Computer vision Part I Berlin Heidelberg 2010 ECCV'10, 157170
Cited by
Sorted by citations
A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring
Satish, P., Srikantaswamy, M., & Ramaswamy, N. (2020) · Traitement du signal · doi:10.18280/ts.370321 · Cited by 59
Metadata
- Authors
- Sroubek, F., Smidl, V., & Kotera, J.
- Affiliations
- Czech Academy of Sciences (CZ)
- ORCID
- Kotera, Jan
- Venue
- 2014 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2014-10
- Volume
- 7577
- Pages
- 4492-4496
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W1983873692
- Cited by
- 4
- References
- 0
- Citations by year
- 2024: 1 · 2021: 1 · 2020: 1 · 2017: 1
- FWCI
- 0.18
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- public-domain
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- ASEP (submittedVersion, public-domain)
- Funders
- Grantová Agentura, Univerzita Karlova
- Grantová Agentura České Republiky (GA13-29225S)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- blind deconvolution, image priors, automatic relevance determination, image restoration, motion blur, out-of-focus blur, blur kernel estimation, ill-posed inverse problems
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