Blind image motion deblurring with L-0-regularized priors

摘要

Blind motion deblurring from a single image has always been a challenging problem. This paper proposes a blind image motion deblurring method which adopts L-0-regularized priors both in kernel and latent image estimation. A sparse and noiseless kernel and reliable intermediate latent images are generated with this prior constraint. An alternating minimization method is adopted to ensure that latent image and kernel estimation converge at an acceptable time. The proposed method is easy to implement since it does not require any complex filtering strategies to select salient edges which are critical to the explicit salient edges selection methods. The experimental results demonstrate that the proposed method is superior because of the better performance when compared with other state-of-the-art methods and the encouraging results obtained on some challenging examples. (C) 2016 Elsevier Inc. All rights reserved.