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1 Xi-an Jiaotong University Chine 2 LIENS - Laboratoire d-informatique de l-école normale supérieure 3 WILLOW - Models of visual object recognition and scene understanding DI-ENS - Département d-informatique de l-École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548

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 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.

Autor: Jian Sun - Wenfei Cao - Zongben Xu - Jean Ponce -

Fuente: https://hal.archives-ouvertes.fr/


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