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

TLDR

The study aims to estimate and remove non‑uniform motion blur from a single blurry image. The authors use a CNN to predict patch‑level blur distributions, augment candidates with rotated images, enforce spatial smoothness via a Markov random field, and then deblur with a patch‑level prior. Experiments demonstrate that the method accurately estimates and removes complex non‑uniform motion blur, outperforming prior techniques.

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.