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- Publisher Website: 10.1137/070703533
- Scopus: eid_2-s2.0-55149111865
- WOS: WOS:000260847400012
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Article: A fast total variation minimization method for image restoration
| Title | A fast total variation minimization method for image restoration |
|---|---|
| Authors | |
| Keywords | Image restoration Denoising Deblurring Total variation |
| Issue Date | 2008 |
| Citation | Multiscale Modeling and Simulation, 2008, v. 7, n. 2, p. 774-795 How to Cite? |
| Abstract | In this paper, we study a fast total variation minimization method for image restoration. In the proposed method, we use the modified total variation minimization scheme to denoise the deblurred image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show that the quality of restored images by the proposed method is competitive with those restored by the existing total variation restoration methods. We show the convergence of the alternating minimization algorithm and demonstrate that thealgorithm is very efficient. © 2008 Society for Industrial and applied Mathematics. |
| Persistent Identifier | http://hdl.handle.net/10722/276831 |
| ISSN | 2021 Impact Factor: 1.961 2020 SCImago Journal Rankings: 1.037 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Huang, Yumei | - |
| dc.contributor.author | Ng, Michael K. | - |
| dc.contributor.author | Wen, You Wei | - |
| dc.date.accessioned | 2019-09-18T08:34:48Z | - |
| dc.date.available | 2019-09-18T08:34:48Z | - |
| dc.date.issued | 2008 | - |
| dc.identifier.citation | Multiscale Modeling and Simulation, 2008, v. 7, n. 2, p. 774-795 | - |
| dc.identifier.issn | 1540-3459 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/276831 | - |
| dc.description.abstract | In this paper, we study a fast total variation minimization method for image restoration. In the proposed method, we use the modified total variation minimization scheme to denoise the deblurred image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show that the quality of restored images by the proposed method is competitive with those restored by the existing total variation restoration methods. We show the convergence of the alternating minimization algorithm and demonstrate that thealgorithm is very efficient. © 2008 Society for Industrial and applied Mathematics. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Multiscale Modeling and Simulation | - |
| dc.subject | Image restoration | - |
| dc.subject | Denoising | - |
| dc.subject | Deblurring | - |
| dc.subject | Total variation | - |
| dc.title | A fast total variation minimization method for image restoration | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1137/070703533 | - |
| dc.identifier.scopus | eid_2-s2.0-55149111865 | - |
| dc.identifier.volume | 7 | - |
| dc.identifier.issue | 2 | - |
| dc.identifier.spage | 774 | - |
| dc.identifier.epage | 795 | - |
| dc.identifier.eissn | 1540-3467 | - |
| dc.identifier.isi | WOS:000260847400012 | - |
| dc.identifier.issnl | 1540-3459 | - |