The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning a convolutional neural network for non-uniform motion blur removal
Open accessAbstract
In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
A Machine Learning Approach for Non-blind Image Deconvolution
Schuler, C. J., Burger, H. C., Harmeling, S., & Scholkopf, B. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.142 · Cited by 319
Cited by
Scale-Recurrent Network for Deep Image Deblurring
Tao, X., Gao, H., Shen, X., Wang, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00853 · Cited by 1,316
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
Video Frame Interpolation via Adaptive Convolution
Niklaus, S., Mai, L., & Liu, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.244 · Cited by 499
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition
Ding, C., & Tao, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2700390 · Cited by 441
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
Zhang, J., Pan, J., Ren, J., Song, Y., Bao, L., Lau, R. W. H., & Yang, M.-H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00267 · Cited by 384
Metadata
- Authors
- Sun, J., Cao, W., Xu, Z., & Ponce, J.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 769-777
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1916935112
- Cited by
- 906
- References
- 4
- FWCI
- 17.46
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sun, J., Cao, W., Xu, Z., & Ponce, J. (2015). Learning a convolutional neural network for non-uniform motion blur removal. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298677
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