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- Publisher Website: 10.1117/1.OE.54.1.013107
- Scopus: eid_2-s2.0-84922032856
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Article: Retinex image enhancement via a learned dictionary
Title | Retinex image enhancement via a learned dictionary |
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Authors | |
Keywords | Retinex Total variation Sparse and redundant representations Learned dictionaries Image enhancement |
Issue Date | 2015 |
Citation | Optical Engineering, 2015, v. 54, n. 1, article no. 013107 How to Cite? |
Abstract | © Society of Photo-Optical Instrumentation Engineers. The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary. This approach is different from existing variational methods, and the advantage of this approach is that the reflectance component in the Retinex model can be represented with more details by the dictionary. A variational method based on the dynamic dictionaries is adopted here, where it changes with respect to iterations of the enhancement algorithm. Numerical examples are also reported to demonstrate that the proposed methods can provide better visual quality of the enhanced high-contrast images than the other variational methods, i.e., revealing more details in the low-light part. |
Persistent Identifier | http://hdl.handle.net/10722/277018 |
ISSN | 2023 Impact Factor: 1.1 2023 SCImago Journal Rankings: 0.331 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chang, Huibin | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Wang, Wei | - |
dc.contributor.author | Zeng, Tieyong | - |
dc.date.accessioned | 2019-09-18T08:35:21Z | - |
dc.date.available | 2019-09-18T08:35:21Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Optical Engineering, 2015, v. 54, n. 1, article no. 013107 | - |
dc.identifier.issn | 0091-3286 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277018 | - |
dc.description.abstract | © Society of Photo-Optical Instrumentation Engineers. The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary. This approach is different from existing variational methods, and the advantage of this approach is that the reflectance component in the Retinex model can be represented with more details by the dictionary. A variational method based on the dynamic dictionaries is adopted here, where it changes with respect to iterations of the enhancement algorithm. Numerical examples are also reported to demonstrate that the proposed methods can provide better visual quality of the enhanced high-contrast images than the other variational methods, i.e., revealing more details in the low-light part. | - |
dc.language | eng | - |
dc.relation.ispartof | Optical Engineering | - |
dc.subject | Retinex | - |
dc.subject | Total variation | - |
dc.subject | Sparse and redundant representations | - |
dc.subject | Learned dictionaries | - |
dc.subject | Image enhancement | - |
dc.title | Retinex image enhancement via a learned dictionary | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1117/1.OE.54.1.013107 | - |
dc.identifier.scopus | eid_2-s2.0-84922032856 | - |
dc.identifier.volume | 54 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 013107 | - |
dc.identifier.epage | article no. 013107 | - |
dc.identifier.eissn | 1560-2303 | - |
dc.identifier.isi | WOS:000349442900018 | - |
dc.identifier.issnl | 0091-3286 | - |