Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal

Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce

CVPR 2015

doi:10.1109/CVPR.2015.7298677 /cvpr/2015/sun2015cvpr-learning/

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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Cite

Text

Sun et al. "Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298677

Markdown

[Sun et al. "Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/sun2015cvpr-learning/) doi:10.1109/CVPR.2015.7298677

BibTeX

@inproceedings{sun2015cvpr-learning,
  title     = {{Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal}},
  author    = {Sun, Jian and Cao, Wenfei and Xu, Zongben and Ponce, Jean},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298677},
  url       = {https://mlanthology.org/cvpr/2015/sun2015cvpr-learning/}
}