Bi-l0-l2-norm regularization for blind motion deblurring
2015 (English)In: Journal of Visual Communication and Image Representation, ISSN 1047-3203, E-ISSN 1095-9076, Vol. 33, 42-59 p.Article in journal (Refereed) Published
In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l<inf>0</inf>-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-l<inf>0</inf>-l<inf>2</inf>-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.
Place, publisher, year, edition, pages
2015. Vol. 33, 42-59 p.
Augmented Lagrangian, Blind deblurring, Blur-kernel estimation, Camera shake removal, Image deconvolution, l0
-minimization, Motion deblurring, Operator splitting, Computational efficiency, Constrained optimization, Lagrange multipliers, Numerical methods, Optimization, Augmented Lagrangians, Blur kernel estimations, Camera shake, Image de convolutions, Operator-splitting, Image enhancement
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-175608DOI: 10.1016/j.jvcir.2015.08.017ISI: 000364982700005ScopusID: 2-s2.0-84942101350OAI: oai:DiVA.org:kth-175608DiVA: diva2:866374
QC 201511022015-11-022015-10-192015-12-18Bibliographically approved