A Projected Forward-Backward Algorithm for Constrained Minimization with Applications to Image Inpainting
Suthep Suantai,
Kunrada Kankam and
Prasit Cholamjiak
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Suthep Suantai: Data Science Research Center, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Kunrada Kankam: School of Science, University of Phayao, Phayao 56000, Thailand
Prasit Cholamjiak: School of Science, University of Phayao, Phayao 56000, Thailand
Mathematics, 2021, vol. 9, issue 8, 1-14
Abstract:
In this research, we study the convex minimization problem in the form of the sum of two proper, lower-semicontinuous, and convex functions. We introduce a new projected forward-backward algorithm using linesearch and inertial techniques. We then establish a weak convergence theorem under mild conditions. It is known that image processing such as inpainting problems can be modeled as the constrained minimization problem of the sum of convex functions. In this connection, we aim to apply the suggested method for solving image inpainting. We also give some comparisons to other methods in the literature. It is shown that the proposed algorithm outperforms others in terms of iterations. Finally, we give an analysis on parameters that are assumed in our hypothesis.
Keywords: convex minimization; image inpainting; inertial techniques; weak convergence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:8:p:890-:d:537799
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