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- Publisher Website: 10.1109/JPROC.2009.2037655
- Scopus: eid_2-s2.0-77952740831
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Article: On the role of sparse and redundant representations in image processing
Title | On the role of sparse and redundant representations in image processing |
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Authors | |
Keywords | Deconvolution Denoising Dictionary learning Frames Inpainting Redundant dictionaries Sparse representations Superresolution Wavelets |
Issue Date | 2010 |
Citation | Proceedings of the IEEE, 2010, v. 98, n. 6, p. 972-982 How to Cite? |
Abstract | Much of the progress made in image processing in the past decades can be attributed to better modeling of image content and a wise deployment of these models in relevant applications. This path of models spans from the simple l2-norm smoothness through robust, thus edge preserving, measures of smoothness (e.g. total variation), and until the very recent models that employ sparse and redundant representations. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. We discuss ways to employ these tools for various image-processing tasks and present several applications in which state-of-the-art results are obtained. © 2009 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326819 |
ISSN | 2023 Impact Factor: 23.2 2023 SCImago Journal Rankings: 6.085 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Elad, Michael | - |
dc.contributor.author | Figueiredo, Mário A.T. | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:26:45Z | - |
dc.date.available | 2023-03-31T05:26:45Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of the IEEE, 2010, v. 98, n. 6, p. 972-982 | - |
dc.identifier.issn | 0018-9219 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326819 | - |
dc.description.abstract | Much of the progress made in image processing in the past decades can be attributed to better modeling of image content and a wise deployment of these models in relevant applications. This path of models spans from the simple l2-norm smoothness through robust, thus edge preserving, measures of smoothness (e.g. total variation), and until the very recent models that employ sparse and redundant representations. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. We discuss ways to employ these tools for various image-processing tasks and present several applications in which state-of-the-art results are obtained. © 2009 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE | - |
dc.subject | Deconvolution | - |
dc.subject | Denoising | - |
dc.subject | Dictionary learning | - |
dc.subject | Frames | - |
dc.subject | Inpainting | - |
dc.subject | Redundant dictionaries | - |
dc.subject | Sparse representations | - |
dc.subject | Superresolution | - |
dc.subject | Wavelets | - |
dc.title | On the role of sparse and redundant representations in image processing | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JPROC.2009.2037655 | - |
dc.identifier.scopus | eid_2-s2.0-77952740831 | - |
dc.identifier.volume | 98 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 972 | - |
dc.identifier.epage | 982 | - |
dc.identifier.isi | WOS:000277884900009 | - |