A Dai-Kou-Type method with image de-blurring application

K.  Ahmed; M. Y.  Waziri; A. S.  Halilu; I. A. R.  Moghrab; H.  Abdullahi; S. M.  Ibrahim; Y. B.  Musa; S.  Murtala; A. M.  Awwal; A.  Sambas

doi:10.46481/jnsps.2025.2569

Authors

K. Ahmed
Department of Mathematical Sciences, Bayero University, Kano, Nigeria
M. Y. Waziri
Department of Mathematical Sciences, Bayero University, Kano, Nigeria
A. S. Halilu
Department of Mathematics, Sule Lamido University Kafin Hausa, Nigeria
I. A. R. Moghrab
Information Systems and Technology Department, Kuwait Technical College, Kuwait
H. Abdullahi
Department of Mathematics, Sule Lamido University Kafin Hausa, Nigeria
S. M. Ibrahim
School of Quantitative Science, Universiti Utara Malaysia, Sintok, 06010, Kedah, Malaysia
Y. B. Musa
Department of Mathematics, Sule Lamido University Kafin Hausa, Nigeria
S. Murtala
Department of Mathematics, Federal University, Dutse, Nigeria
A. M. Awwal
Department of Mathematics, Gombe State University, Gombe, Nigeria
A. Sambas
Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Campus Besut, 22200 Terengganu, Malaysia

Keywords:

Nonlinear monotone systems, Spectral parameter, Image de-blurring, Convex constraint

Abstract

By exploiting nice attributes of three-term conjugate gradient (TTCG) methods and the efficiency of the Dai-Kou scheme, this paper proposes a spectral class of Dai-Kou-type methods for monotone systems with convex constraints. The scheme combines a modified Dai-Kou search direction with the projection method and can best be described as an adaptation of the Dai-Kou method to nonlinear systems. An important contribution of the scheme is its application to image de-blurring. The method is shown to converge globally under mild assumptions. Furthermore, test results of some numerical experiments suggest that the proposed approach outperforms three recent schemes for convex constrained monotone systems.

Dimensions

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