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- Publisher Website: 10.1109/TIP.2010.2073474
- Scopus: eid_2-s2.0-79951838700
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Article: Blind deconvolution using generalized cross-validation approach to regularization parameter estimation
Title | Blind deconvolution using generalized cross-validation approach to regularization parameter estimation |
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Authors | |
Keywords | blind deconvolution generalized cross validation (GCV) regularization parameters total variation (TV) Alternating minimization |
Issue Date | 2011 |
Citation | IEEE Transactions on Image Processing, 2011, v. 20, n. 3, p. 670-680 How to Cite? |
Abstract | In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior. © 2011 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276889 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liao, Haiyong | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:34:57Z | - |
dc.date.available | 2019-09-18T08:34:57Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2011, v. 20, n. 3, p. 670-680 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276889 | - |
dc.description.abstract | In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior. © 2011 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | blind deconvolution | - |
dc.subject | generalized cross validation (GCV) | - |
dc.subject | regularization parameters | - |
dc.subject | total variation (TV) | - |
dc.subject | Alternating minimization | - |
dc.title | Blind deconvolution using generalized cross-validation approach to regularization parameter estimation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2010.2073474 | - |
dc.identifier.scopus | eid_2-s2.0-79951838700 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 670 | - |
dc.identifier.epage | 680 | - |
dc.identifier.isi | WOS:000287400700006 | - |
dc.identifier.issnl | 1057-7149 | - |