Work

Learning a convolutional neural network for non-uniform motion blur removal

Year: 2015

Type: conference-paper

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.

Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings

DOI: 10.1109/cvpr.2015.7298677

Authors: Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce

Institutions: Xi'an Jiaotong University, École Normale Supérieure - PSL, Département d'Informatique, WILLOW: Vision par ordinateur incarnée

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Institut Universitaire de France, Program for New Century Excellent Talents in University, National Key Research and Development Program of China