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- Publisher Website: 10.1016/j.cam.2008.05.056
- Scopus: eid_2-s2.0-60349098401
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Article: Approximation BFGS methods for nonlinear image restoration
Title | Approximation BFGS methods for nonlinear image restoration |
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Authors | |
Keywords | Nonlinear image restoration Optimization Regularization |
Issue Date | 2009 |
Citation | Journal of Computational and Applied Mathematics, 2009, v. 226, n. 1, p. 84-91 How to Cite? |
Abstract | We consider the iterative solution of unconstrained minimization problems arising from nonlinear image restoration. Our approach is based on a novel generalized BFGS method for such large-scale image restoration minimization problems. The complexity per step of the method is of O (n log n) operations and only O (n) memory allocations are required, where n is the number of image pixels. Based on the results given in [Carmine Di Fiore, Stefano Fanelli, Filomena Lepore, Paolo Zellini, Matrix algebras in quasi-Newton methods for unconstrained minimization, Numer. Math. 94 (2003) 479-500], we show that the method is globally convergent for our nonlinear image restoration problems. Experimental results are presented to illustrate the effectiveness of the proposed method. © 2008 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/276834 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.858 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lu, Lin Zhang | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Lin, Fu Rong | - |
dc.date.accessioned | 2019-09-18T08:34:48Z | - |
dc.date.available | 2019-09-18T08:34:48Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Journal of Computational and Applied Mathematics, 2009, v. 226, n. 1, p. 84-91 | - |
dc.identifier.issn | 0377-0427 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276834 | - |
dc.description.abstract | We consider the iterative solution of unconstrained minimization problems arising from nonlinear image restoration. Our approach is based on a novel generalized BFGS method for such large-scale image restoration minimization problems. The complexity per step of the method is of O (n log n) operations and only O (n) memory allocations are required, where n is the number of image pixels. Based on the results given in [Carmine Di Fiore, Stefano Fanelli, Filomena Lepore, Paolo Zellini, Matrix algebras in quasi-Newton methods for unconstrained minimization, Numer. Math. 94 (2003) 479-500], we show that the method is globally convergent for our nonlinear image restoration problems. Experimental results are presented to illustrate the effectiveness of the proposed method. © 2008 Elsevier B.V. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Computational and Applied Mathematics | - |
dc.subject | Nonlinear image restoration | - |
dc.subject | Optimization | - |
dc.subject | Regularization | - |
dc.title | Approximation BFGS methods for nonlinear image restoration | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1016/j.cam.2008.05.056 | - |
dc.identifier.scopus | eid_2-s2.0-60349098401 | - |
dc.identifier.volume | 226 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 84 | - |
dc.identifier.epage | 91 | - |
dc.identifier.isi | WOS:000264670200010 | - |
dc.identifier.issnl | 0377-0427 | - |