Image restoration by advanced parallel inertial forward–backward splitting methods
Abstract The paper proposes an advanced inertial forward–backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yambangwai, Damrongsak [verfasserIn] Khan, Suhel Ahmad [verfasserIn] Dutta, Hemen [verfasserIn] Cholamjiak, Watcharaporn [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2021), 8 vom: 13. Feb., Seite 6029-6042 |
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| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:8 ; day:13 ; month:02 ; pages:6029-6042 |
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| DOI / URN: |
10.1007/s00500-021-05596-6 |
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| 520 | |a Abstract The paper proposes an advanced inertial forward–backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. Further, we solve unconstrained image recovery problems and the quality of the proposed algorithm has also been demonstrated for common types of blur effects. | ||
| 650 | 4 | |a Image restorations |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Khan, Suhel Ahmad |e verfasserin |4 aut | |
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Abstract The paper proposes an advanced inertial forward–backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. Further, we solve unconstrained image recovery problems and the quality of the proposed algorithm has also been demonstrated for common types of blur effects. |
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Abstract The paper proposes an advanced inertial forward–backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. Further, we solve unconstrained image recovery problems and the quality of the proposed algorithm has also been demonstrated for common types of blur effects. |
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Abstract The paper proposes an advanced inertial forward–backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. Further, we solve unconstrained image recovery problems and the quality of the proposed algorithm has also been demonstrated for common types of blur effects. |
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Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. 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