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

Autor:	Sun, Jian, Cao, Wenfei, Xu, Zongben, Ponce, Jean
Rok vydání:	2015
Předmět:	Computer Science - Computer Vision and Pattern Recognition I.4
Druh dokumentu:	Working Paper
Popis:	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. Comment: This is a final version accepted by CVPR 2015
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1503.00593 Zobrazit plný text záznamu View this record from Arxiv