Learning a convolutional neural network for non-uniform motion blur removal - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

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

Résumé

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 de-blurring 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.
Fichier principal
Vignette du fichier
sun2015.pdf (8.97 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01250478 , version 1 (04-01-2016)

Identifiants

Citer

Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce. Learning a convolutional neural network for non-uniform motion blur removal. CVPR 2015 - IEEE Conference on Computer Vision and Pattern Recognition 2015, Jun 2015, Boston, United States. ⟨10.1109/CVPR.2015.7298677⟩. ⟨hal-01250478⟩
224 Consultations
705 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More