Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
This addresses image quality issues for photography and computer vision applications, but is incremental as it builds on existing deblurring techniques.
The paper tackles the problem of estimating and removing non-uniform motion blur from a single blurry image, achieving effective removal of complex blur not handled well by prior methods.
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.