Share Email Print

Proceedings Paper

Image deblurring based on nonlocal regularization with a non-convex sparsity constraint
Author(s): Simiao Zhu; Zhenming Su; Lian Li; Yi Yang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In recent years, nonlocal regularization methods for image restoration (IR) have drawn more and more attention due to the promising results obtained when compared to the traditional local regularization methods. Despite the success of this technique, in order to obtain computational efficiency, a convex regularizing functional is exploited in most existing methods, which is equivalent to imposing a convex prior on the nonlocal difference operator output. However, our conducted experiment illustrates that the empirical distribution of the output of the nonlocal difference operator especially in the seminal work of Kheradmand et al. should be characterized with an extremely heavy-tailed distribution rather than a convex distribution. Therefore, in this paper, we propose a nonlocal regularization-based method with a non-convex sparsity constraint for image deblurring. Finally, an effective algorithm is developed to solve the corresponding non-convex optimization problem. The experimental results demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152F (10 April 2018); doi: 10.1117/12.2302490
Show Author Affiliations
Simiao Zhu, Lanzhou Univ. (China)
Zhenming Su, Lanzhou Univ. (China)
Lian Li, Lanzhou Univ. (China)
Yi Yang, Lanzhou Univ. (China)

Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

© SPIE. Terms of Use
Back to Top