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- Publisher Website: 10.1080/00207160.2012.688821
- Scopus: eid_2-s2.0-84873302061
- WOS: WOS:000313782200004
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Article: A fast minimization method for blur and multiplicative noise removal
Title | A fast minimization method for blur and multiplicative noise removal |
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Authors | |
Keywords | Iterative method image restoration Blur Multiplicative noise minimization |
Issue Date | 2013 |
Citation | International Journal of Computer Mathematics, 2013, v. 90, n. 1, p. 48-61 How to Cite? |
Abstract | Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l 1-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio. © 2013 Copyright Taylor and Francis Group, LLC. |
Persistent Identifier | http://hdl.handle.net/10722/276669 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.502 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Fan | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:34:18Z | - |
dc.date.available | 2019-09-18T08:34:18Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | International Journal of Computer Mathematics, 2013, v. 90, n. 1, p. 48-61 | - |
dc.identifier.issn | 0020-7160 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276669 | - |
dc.description.abstract | Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l 1-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio. © 2013 Copyright Taylor and Francis Group, LLC. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Computer Mathematics | - |
dc.subject | Iterative method | - |
dc.subject | image restoration | - |
dc.subject | Blur | - |
dc.subject | Multiplicative noise | - |
dc.subject | minimization | - |
dc.title | A fast minimization method for blur and multiplicative noise removal | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/00207160.2012.688821 | - |
dc.identifier.scopus | eid_2-s2.0-84873302061 | - |
dc.identifier.volume | 90 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 48 | - |
dc.identifier.epage | 61 | - |
dc.identifier.eissn | 1029-0265 | - |
dc.identifier.isi | WOS:000313782200004 | - |
dc.identifier.issnl | 0020-7160 | - |