Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal
Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce
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.
BibTeX
@inproceedings{cvpr2015_learningaconvolu,
title = {Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal},
author = {Jian Sun and Wenfei Cao and Zongben Xu and Jean Ponce},
booktitle = {CVPR 2015},
year = {2015}
}