Bi-l0-l2-norm regularization for blind motion deblurring

• Published in: Journal of visual communication and image representation 2015-11, Vol.33, p.42-59
• Main Authors: Shao, Wen-Ze, Li, Hai-Bo, Elad, Michael
• Format: Article
• Language: English
• Subjects: Augmented Lagrangian; Augmented Lagrangians; Blind deblurring; Blur kernel estimations; Blur-kernel estimation; Camera shake; Camera shake removal; Computational efficiency; Constrained optimization; Image de convolutions; Image deconvolution; Image enhancement; l 0 -l 2 -minimization; l0-l2-minimization; Lagrange multipliers; Motion deblurring; Numerical methods; Operator splitting; Optimization
• ISSN: 1047-3203; 1095-9076; 1095-9076
• DOI: 10.1016/j.jvcir.2015.08.017

•A simple blur-kernel estimation method is developed for blind motion deblurring.•The method is regularized by the newly proposed bi-l0-l2-norm regularization.•The sharp image and the blur-kernel are estimated very efficiently using FFT.•Leading performance is achieved in both terms of speed and output quality. In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel. Compared with existing methods, the proposed regularization is shown to be more effective and robust, leading to a more accurate motion blur-kernel and a better final restored image. A fast numerical scheme is deployed for alternatingly computing the sharp image and the blur-kernel, by coupling the operator splitting and augmented Lagrangian methods. Experimental results on both a benchmark image dataset and real-world motion blurred images show that the proposed approach is highly competitive with state-of-the-art methods in both deblurring effectiveness and computational efficiency.