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Learning a convolutional neural network for non-uniform motion blur removal
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.
Cited literature [31 references]
https://hal.inria.fr/hal-01250478
Contributor : Minsu Cho <>
Submitted on : Monday, January 4, 2016 - 7:15:45 PM
Last modification on : Thursday, July 1, 2021 - 5:58:07 PM
Long-term archiving on: : Friday, April 15, 2016 - 4:21:28 PM
Contributor : Minsu Cho <>
Submitted on : Monday, January 4, 2016 - 7:15:45 PM
Last modification on : Thursday, July 1, 2021 - 5:58:07 PM
Long-term archiving on: : Friday, April 15, 2016 - 4:21:28 PM
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sun2015.pdf
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