Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees

Hong-Xia Dou, Ting-Zhu Huang, Xi-Le Zhao, Jie Huang and Jun Liu

Applied Mathematics and Computation, 2020, vol. 377, issue C

Abstract: The semi-blind image deblurring problem aims to simultaneously estimate the clean image and the point spread function (PSF), which results in a (jointly) nonconvex optimization problem. In this paper, we develop an efficient algorithm to tackle the corresponding minimization problem based on the framework of the proximal alternating minimization (PAM). We also establish the convergence of the proposed algorithm under a mild assumption. Numerical experiments demonstrate our approach could obtain a more robust performance than the related state-of-the-art semi-blind image deblurring method.

Keywords: Proximal method; Nonconvex optimization problem; Semi-blind image deblurring; Convergence analysis (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:377:y:2020:i:c:s0096300320301375

DOI: 10.1016/j.amc.2020.125168

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