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- Scopus: eid_2-s2.0-80054887691
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Article: Compression and denoising using l0-norm
Title | Compression and denoising using l0-norm |
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Authors | |
Issue Date | 2011 |
Citation | Computational Optimization and Applications, 2011, v. 50, n. 2, p. 425-444 How to Cite? |
Abstract | In this paper, we deal with l 0-norm data fitting and total variation regularization for image compression and denoising. The l 0-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l 0-norm, it is usually approximated by other functions such as the l 1-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l 0-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm. © 2010 Springer Science+Business Media, LLC. |
Persistent Identifier | http://hdl.handle.net/10722/276910 |
ISSN | 2021 Impact Factor: 2.005 2020 SCImago Journal Rankings: 1.028 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yau, Andy C. | - |
dc.contributor.author | Tai, Xuecheng | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:35:02Z | - |
dc.date.available | 2019-09-18T08:35:02Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Computational Optimization and Applications, 2011, v. 50, n. 2, p. 425-444 | - |
dc.identifier.issn | 0926-6003 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276910 | - |
dc.description.abstract | In this paper, we deal with l 0-norm data fitting and total variation regularization for image compression and denoising. The l 0-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l 0-norm, it is usually approximated by other functions such as the l 1-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l 0-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm. © 2010 Springer Science+Business Media, LLC. | - |
dc.language | eng | - |
dc.relation.ispartof | Computational Optimization and Applications | - |
dc.title | Compression and denoising using l0-norm | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10589-010-9352-4 | - |
dc.identifier.scopus | eid_2-s2.0-80054887691 | - |
dc.identifier.volume | 50 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 425 | - |
dc.identifier.epage | 444 | - |
dc.identifier.eissn | 1573-2894 | - |
dc.identifier.isi | WOS:000295574600011 | - |
dc.identifier.issnl | 0926-6003 | - |