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Article: Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal
Title | Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal |
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Authors | |
Issue Date | 2017 |
Citation | Applied Optics, 2017, v. 56, n. 9, p. D47-D51 How to Cite? |
Abstract | © 2017 Optical Society of America. In this paper, we propose an alternating minimization algorithm with an automatic selection of the regularization parameter for image reconstruction of photon-counted images. By using the generalized cross-validation technique, the regularization parameter can be updated in the iterations of the alternating minimization algorithm. Experimental results show that our proposed algorithm outperforms the two existing methods, the maximum likelihood expectation maximization estimator with total variation regularization and the primal dual method, where the parameters must be set in advance. |
Persistent Identifier | http://hdl.handle.net/10722/277066 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.487 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Xiongjun | - |
dc.contributor.author | Javidi, Bahram | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:35:30Z | - |
dc.date.available | 2019-09-18T08:35:30Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Applied Optics, 2017, v. 56, n. 9, p. D47-D51 | - |
dc.identifier.issn | 1559-128X | - |
dc.identifier.uri | http://hdl.handle.net/10722/277066 | - |
dc.description.abstract | © 2017 Optical Society of America. In this paper, we propose an alternating minimization algorithm with an automatic selection of the regularization parameter for image reconstruction of photon-counted images. By using the generalized cross-validation technique, the regularization parameter can be updated in the iterations of the alternating minimization algorithm. Experimental results show that our proposed algorithm outperforms the two existing methods, the maximum likelihood expectation maximization estimator with total variation regularization and the primal dual method, where the parameters must be set in advance. | - |
dc.language | eng | - |
dc.relation.ispartof | Applied Optics | - |
dc.title | Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1364/AO.56.000D47 | - |
dc.identifier.scopus | eid_2-s2.0-85015831914 | - |
dc.identifier.volume | 56 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | D47 | - |
dc.identifier.epage | D51 | - |
dc.identifier.eissn | 2155-3165 | - |
dc.identifier.isi | WOS:000398087100007 | - |
dc.identifier.issnl | 1559-128X | - |