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- Publisher Website: 10.1109/ICASSP.2010.5494994
- Scopus: eid_2-s2.0-78049384975
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Conference Paper: Fast total variation image restoration with parameter estimation using Bayesian inference
Title | Fast total variation image restoration with parameter estimation using Bayesian inference |
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Authors | |
Keywords | Bayesian methods Parameter estimation Total variation Variational methods Image restoration |
Issue Date | 2010 |
Citation | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2010, p. 770-773 How to Cite? |
Abstract | In this paper we propose two fast Total Variation (TV) based algorithms for image restoration by utilizing variational posterior distribution approximation. The unknown image and the hyperparameters for the image and observation models are formulated and estimated simultaneously within a hierachical Bayesian framework, rendering the algorithms fully-automated without any free parameters. Experimental results demonstrate that the proposed algorithms provide restoration results competitive to existing methods in terms of image quality while achieving superior computational efficiency. ©2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276878 |
ISSN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Amizic, Bruno | - |
dc.contributor.author | Babacan, S. Derin | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Molina, Rafael | - |
dc.contributor.author | Katsaggelos, Aggelos K. | - |
dc.date.accessioned | 2019-09-18T08:34:55Z | - |
dc.date.available | 2019-09-18T08:34:55Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2010, p. 770-773 | - |
dc.identifier.issn | 1520-6149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276878 | - |
dc.description.abstract | In this paper we propose two fast Total Variation (TV) based algorithms for image restoration by utilizing variational posterior distribution approximation. The unknown image and the hyperparameters for the image and observation models are formulated and estimated simultaneously within a hierachical Bayesian framework, rendering the algorithms fully-automated without any free parameters. Experimental results demonstrate that the proposed algorithms provide restoration results competitive to existing methods in terms of image quality while achieving superior computational efficiency. ©2010 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | - |
dc.subject | Bayesian methods | - |
dc.subject | Parameter estimation | - |
dc.subject | Total variation | - |
dc.subject | Variational methods | - |
dc.subject | Image restoration | - |
dc.title | Fast total variation image restoration with parameter estimation using Bayesian inference | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICASSP.2010.5494994 | - |
dc.identifier.scopus | eid_2-s2.0-78049384975 | - |
dc.identifier.spage | 770 | - |
dc.identifier.epage | 773 | - |
dc.identifier.isi | WOS:000287096000183 | - |
dc.identifier.issnl | 1520-6149 | - |