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- Publisher Website: 10.1007/s10915-017-0597-2
- Scopus: eid_2-s2.0-85033594941
- WOS: WOS:000431399600013
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Article: A Fast Algorithm for Deconvolution and Poisson Noise Removal
Title | A Fast Algorithm for Deconvolution and Poisson Noise Removal |
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Authors | |
Keywords | Kullback-Leibler divergence Alternating minimization algorithm Total variation Poisson noise |
Issue Date | 2018 |
Citation | Journal of Scientific Computing, 2018, v. 75, n. 3, p. 1535-1554 How to Cite? |
Abstract | © 2017, Springer Science+Business Media, LLC. Poisson noise removal problems have attracted much attention in recent years. The main aim of this paper is to study and propose an alternating minimization algorithm for Poisson noise removal with nonnegative constraint. The algorithm minimizes the sum of a Kullback-Leibler divergence term and a total variation term. We derive the algorithm by utilizing the quadratic penalty function technique. Moreover, the convergence of the proposed algorithm is also established under very mild conditions. Numerical comparisons between our approach and several state-of-the-art algorithms are presented to demonstrate the efficiency of our proposed algorithm. |
Persistent Identifier | http://hdl.handle.net/10722/276563 |
ISSN | 2023 Impact Factor: 2.8 2023 SCImago Journal Rankings: 1.248 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Xiongjun | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Bai, Minru | - |
dc.date.accessioned | 2019-09-18T08:33:59Z | - |
dc.date.available | 2019-09-18T08:33:59Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Scientific Computing, 2018, v. 75, n. 3, p. 1535-1554 | - |
dc.identifier.issn | 0885-7474 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276563 | - |
dc.description.abstract | © 2017, Springer Science+Business Media, LLC. Poisson noise removal problems have attracted much attention in recent years. The main aim of this paper is to study and propose an alternating minimization algorithm for Poisson noise removal with nonnegative constraint. The algorithm minimizes the sum of a Kullback-Leibler divergence term and a total variation term. We derive the algorithm by utilizing the quadratic penalty function technique. Moreover, the convergence of the proposed algorithm is also established under very mild conditions. Numerical comparisons between our approach and several state-of-the-art algorithms are presented to demonstrate the efficiency of our proposed algorithm. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Scientific Computing | - |
dc.subject | Kullback-Leibler divergence | - |
dc.subject | Alternating minimization algorithm | - |
dc.subject | Total variation | - |
dc.subject | Poisson noise | - |
dc.title | A Fast Algorithm for Deconvolution and Poisson Noise Removal | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10915-017-0597-2 | - |
dc.identifier.scopus | eid_2-s2.0-85033594941 | - |
dc.identifier.volume | 75 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 1535 | - |
dc.identifier.epage | 1554 | - |
dc.identifier.isi | WOS:000431399600013 | - |
dc.identifier.issnl | 0885-7474 | - |