An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing
This study introduces an innovative approach to address convex optimization problems, with a specific focus on applications in image and signal processing. The research aims to develop a self-adaptive extra proximal algorithm that incorporates an inertial term to effectively tackle challenges in con...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023077216 |
| _version_ | 1797646502935396352 |
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| author | Joshua Olilima Adesanmi Mogbademu M. Asif Memon Adebowale Martins Obalalu Hudson Akewe Jamel Seidu |
| author_facet | Joshua Olilima Adesanmi Mogbademu M. Asif Memon Adebowale Martins Obalalu Hudson Akewe Jamel Seidu |
| author_sort | Joshua Olilima |
| collection | DOAJ |
| description | This study introduces an innovative approach to address convex optimization problems, with a specific focus on applications in image and signal processing. The research aims to develop a self-adaptive extra proximal algorithm that incorporates an inertial term to effectively tackle challenges in convex optimization. The study's significance lies in its contribution to advancing optimization techniques in the realm of image deblurring and signal reconstruction. The proposed methodology involves creating a novel self-adaptive extra proximal algorithm, analyzing its convergence rigorously to ensure reliability and effectiveness. Numerical examples, including image deblurring and signal reconstruction tasks using only 10% of the original signal, illustrate the practical applicability and advantages of the algorithm. By introducing an inertial term within the extra proximal framework, the algorithm demonstrates potential for faster convergence and improved optimization outcomes, addressing real-world challenges of image enhancement and signal reconstruction. The algorithm's incorporation of an inertial term showcases its potential for faster convergence and improved optimization outcomes. This research significantly contributes to the field of optimization techniques, particularly in the context of image and signal processing applications. |
| first_indexed | 2024-03-11T15:03:31Z |
| format | Article |
| id | doaj.art-33e46f4b6bfc46e9bf239c641daa6c97 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:03:31Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-33e46f4b6bfc46e9bf239c641daa6c972023-10-30T06:06:24ZengElsevierHeliyon2405-84402023-10-01910e20513An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processingJoshua Olilima0Adesanmi Mogbademu1M. Asif Memon2Adebowale Martins Obalalu3Hudson Akewe4Jamel Seidu5Department of Mathematical Sciences, Augustine University, Ilara-Epe, Lagos, NigeriaDepartment of Mathematics, University of Lagos, Akoka, Lagos, NigeriaDepartment of Mathematics and Social Sciences, Sukkur IBA University, Sukkur, 65200, Sindh, PakistanDepartment of Mathematical Sciences, Augustine University, Ilara-Epe, Lagos, NigeriaDepartment of Mathematics, University of Lagos, Akoka, Lagos, NigeriaSchool of Railways and Infrastructure Development, University of Mines and Technology (UMaT) Essikado, Sekondi-Takoradi, Ghana; Corresponding author.This study introduces an innovative approach to address convex optimization problems, with a specific focus on applications in image and signal processing. The research aims to develop a self-adaptive extra proximal algorithm that incorporates an inertial term to effectively tackle challenges in convex optimization. The study's significance lies in its contribution to advancing optimization techniques in the realm of image deblurring and signal reconstruction. The proposed methodology involves creating a novel self-adaptive extra proximal algorithm, analyzing its convergence rigorously to ensure reliability and effectiveness. Numerical examples, including image deblurring and signal reconstruction tasks using only 10% of the original signal, illustrate the practical applicability and advantages of the algorithm. By introducing an inertial term within the extra proximal framework, the algorithm demonstrates potential for faster convergence and improved optimization outcomes, addressing real-world challenges of image enhancement and signal reconstruction. The algorithm's incorporation of an inertial term showcases its potential for faster convergence and improved optimization outcomes. This research significantly contributes to the field of optimization techniques, particularly in the context of image and signal processing applications.http://www.sciencedirect.com/science/article/pii/S2405844023077216Forward-backward algorithmSignal & image processingConvex minimization problemWeak convergenceInertial method |
| spellingShingle | Joshua Olilima Adesanmi Mogbademu M. Asif Memon Adebowale Martins Obalalu Hudson Akewe Jamel Seidu An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing Heliyon Forward-backward algorithm Signal & image processing Convex minimization problem Weak convergence Inertial method |
| title | An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| title_full | An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| title_fullStr | An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| title_full_unstemmed | An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| title_short | An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| title_sort | innovative inertial extra proximal gradient algorithm for solving convex optimization problems with application to image and signal processing |
| topic | Forward-backward algorithm Signal & image processing Convex minimization problem Weak convergence Inertial method |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023077216 |
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