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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 769-777
Jian Sun , Xi'an Jiaotong University, China
Wenfei Cao , Xi'an Jiaotong University, China
Zongben Xu , Xi'an Jiaotong University, China
Jean Ponce , École Normale Supérieure / PSL Research University, France
ABSTRACT
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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CITATION

J. Sun, Wenfei Cao, Zongben Xu and J. Ponce, "Learning a convolutional neural network for non-uniform motion blur removal," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 769-777.
doi:10.1109/CVPR.2015.7298677
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