Image deblurring based on nonlocal regularization with a non-convex sparsity constraint

Simiao Zhu; Zhenming Su; Lian Li; Yi Yang

doi:10.1117/12.2302490

10 April 2018 Image deblurring based on nonlocal regularization with a non-convex sparsity constraint

Simiao Zhu, Zhenming Su, Lian Li, Yi Yang

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152F (2018) https://doi.org/10.1117/12.2302490
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

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.

Citation Download Citation

Simiao Zhu, Zhenming Su, Lian Li, and Yi Yang "Image deblurring based on nonlocal regularization with a non-convex sparsity constraint", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152F (10 April 2018); https://doi.org/10.1117/12.2302490

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