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- Publisher Website: 10.1109/TIP.2012.2205007
- Scopus: eid_2-s2.0-84867880275
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Article: Multiplicative noise removal via a learned dictionary
Title | Multiplicative noise removal via a learned dictionary |
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Authors | |
Keywords | dictionary multiplicative noise Denoising sparse representation variational model |
Issue Date | 2012 |
Citation | IEEE Transactions on Image Processing, 2012, v. 21, n. 11, p. 4534-4543 How to Cite? |
Abstract | Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods. © 2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276935 |
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 | Huang, Yu Mei | - |
dc.contributor.author | Moisan, Lionel | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Zeng, Tieyong | - |
dc.date.accessioned | 2019-09-18T08:35:06Z | - |
dc.date.available | 2019-09-18T08:35:06Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2012, v. 21, n. 11, p. 4534-4543 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276935 | - |
dc.description.abstract | Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods. © 2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | dictionary | - |
dc.subject | multiplicative noise | - |
dc.subject | Denoising | - |
dc.subject | sparse representation | - |
dc.subject | variational model | - |
dc.title | Multiplicative noise removal via a learned dictionary | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2012.2205007 | - |
dc.identifier.scopus | eid_2-s2.0-84867880275 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 4534 | - |
dc.identifier.epage | 4543 | - |
dc.identifier.isi | WOS:000310140700004 | - |
dc.identifier.issnl | 1057-7149 | - |