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Mathematical Problems in Engineering
Volume 2012, Article ID 639824, 10 pages
Research Article

Kernel Optimization for Blind Motion Deblurring with Image Edge Prior

College of Computing & Communication Engineering, Graduate University of Chinese Academy of Science, Beijing 100049, China

Received 10 January 2012; Accepted 20 February 2012

Academic Editor: Ming Li

Copyright © 2012 Jing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image motion deblurring with unknown blur kernel is an ill-posed problem. This paper proposes a blind motion deblurring approach that solves blur kernel and the latent image robustly. For kernel optimization, an edge mask is used as an image prior to improve kernel update, then an edge selection mask is adopted to improve image update. In addition, an alternative iterative method is introduced to perform kernel optimization under a multiscale scheme. Moreover, for image restoration, a total-variation-(TV-) based algorithm is proposed to recover the latent image via nonblind deconvolution. Experimental results demonstrate that our method obtains accurate blur kernel and achieves better deblurring results than previous works.