The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Edge-based blur kernel estimation using patch priors
Abstract
Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a “trusted” subset of x by imposing a patch prior specifically tailored towards modeling the appearance of image edge and corner primitives. To choose proper patch priors we examine both statistical priors learned from a natural image dataset and a simple patch prior from synthetic structures. Based on the patch priors, we iteratively recover the partial latent image x and the blur kernel k. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images.
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Cites
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,424
Removing camera shake from a single photograph
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141956 · Cited by 1,815
From learning models of natural image patches to whole image restoration
Zoran, D., & Weiss, Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126278 · Cited by 1,522
High-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
Fast Image Deconvolution using Hyper-Laplacian Priors
Krishnan, D., & Fergus, R. (2009) · Cited by 1,205
Blind deconvolution using a normalized sparsity measure
Krishnan, D., Tay, T., & Fergus, R. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995521 · Cited by 1,119
Two-Phase Kernel Estimation for Robust Motion Deblurring
Xu, L., & Jia, J. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15549-9_12 · Cited by 1,027
Cited by
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
Blind Deblurring Using Internal Patch Recurrence
Michaeli, T., & Irani, M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_51 · Cited by 403
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
Rim, J., Lee, H., Won, J., & Cho, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58595-2_12 · Cited by 373
Self-citationImage Deblurring via Extreme Channels Prior
Yan, Y., Ren, W., Guo, Y., Wang, R., & Cao, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.738 · Cited by 365
Shen, Z., Wang, W., Lu, X., Shen, J., Ling, H., Xu, T., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00567 · Cited by 359
Metadata
- Authors
- Sun, L., Cho, S., Wang, J., & Hays, J.
- Venue
- IEEE International Conference on Computational Photography (ICCP)
- Publisher
- IEEE
- Published
- 2013-04
- Pages
- 1-8
- OpenAlex
- W2032506791
- Cited by
- 440
- References
- 19
- FWCI
- 14.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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