The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Efficient marginal likelihood optimization in blind deconvolution
Open accessAbstract
In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAPx, kstrategies which estimate the mode pair x, k and often lead to undesired results, and MAPkstrategies which select the best k while marginalizing over all possible x images. The MAPkprinciple is significantly more robust than the MAPx, kone, yet, it involves a challenging marginalization over latent images. As a result, MAPktechniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAPkalgorithm which involves only a modest modification of common MAPx, kalgorithms. We show that MAPkcan, in fact, be optimized easily, with no additional computational complexity.
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Cites
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Self-citationHigh-quality motion deblurring from a single image
Shan, Q., Jia, J., & Agarwala, A. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360672 · Cited by 1,386
Understanding and evaluating blind deconvolution algorithms
Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206815 · Cited by 1,208
Self-citationFast Image Deconvolution using Hyper-Laplacian Priors
Krishnan, D., & Fergus, R. (2009) · Cited by 1,205
Two-Phase Kernel Estimation for Robust Motion Deblurring
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Image and depth from a conventional camera with a coded aperture
Levin, A., Fergus, R., Durand, F., & Freeman, W. T. (2007) · ACM SIGGRAPH 2007 papers · doi:10.1145/1275808.1276464 · Cited by 1,023
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PSF estimation using sharp edge prediction
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Unnatural L0 Sparse Representation for Natural Image Deblurring
Xu, L., Zheng, S., & Jia, J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.147 · Cited by 1,079
Learning a convolutional neural network for non-uniform motion blur removal
Sun, J., Cao, W., Xu, Z., & Ponce, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298677 · Cited by 906
Schuler, C. J., Hirsch, M., Harmeling, S., & Schölkopf, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2481418 · Cited by 560
Shrinkage Fields for Effective Image Restoration
Schmidt, U., & Roth, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.349 · Cited by 543
Edge-based blur kernel estimation using patch priors
Sun, L., Cho, S., Wang, J., & Hays, J. (2013) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2013.6528301 · Cited by 440
Metadata
- Authors
- Levin, A., Weiss, Y., Durand, F., & Freeman, W. T.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 2657-2664
- OpenAlex
- W2036682493
- Cited by
- 628
- References
- 23
- FWCI
- 29.18
- Type
- conference paper
- Language
- en
- Open access
- green
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