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] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 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 |
| Links: |
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| DOI / URN: |
10.1007/s00500-021-05596-6 |
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OLC2124609998 |
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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. | ||
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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. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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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. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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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. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124609998</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505091710.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-021-05596-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124609998</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00500-021-05596-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yambangwai, Damrongsak</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image restoration by advanced parallel inertial forward–backward splitting methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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