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- Publisher Website: 10.1109/TIP.2015.2401430
- Scopus: eid_2-s2.0-84925114263
- WOS: WOS:000351463700004
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Article: On the convergence of nonconvex minimization methods for image recovery
Title | On the convergence of nonconvex minimization methods for image recovery |
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Authors | |
Keywords | Kurdykalojasiewicz inequality nonconvex and nonsmooth alternating minimization methods box-constraints Image restoration |
Issue Date | 2015 |
Citation | IEEE Transactions on Image Processing, 2015, v. 24, n. 5, p. 1587-1598 How to Cite? |
Abstract | © 2015 IEEE. Nonconvex nonsmooth regularization method has been shown to be effective for restoring images with neat edges. Fast alternating minimization schemes have also been proposed and developed to solve the nonconvex nonsmooth minimization problem. The main contribution of this paper is to show the convergence of these alternating minimization schemes, based on the Kurdyka-Łojasiewicz property. In particular, we show that the iterates generated by the alternating minimization scheme, converges to a critical point of this nonconvex nonsmooth objective function. We also extend the analysis to nonconvex nonsmooth regularization model with box constraints, and obtain similar convergence results of the related minimization algorithm. Numerical examples are given to illustrate our convergence analysis. |
Persistent Identifier | http://hdl.handle.net/10722/276684 |
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 | Xiao, Jin | - |
dc.contributor.author | Ng, Michael Kwok Po | - |
dc.contributor.author | Yang, Yu Fei | - |
dc.date.accessioned | 2019-09-18T08:34:21Z | - |
dc.date.available | 2019-09-18T08:34:21Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2015, v. 24, n. 5, p. 1587-1598 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276684 | - |
dc.description.abstract | © 2015 IEEE. Nonconvex nonsmooth regularization method has been shown to be effective for restoring images with neat edges. Fast alternating minimization schemes have also been proposed and developed to solve the nonconvex nonsmooth minimization problem. The main contribution of this paper is to show the convergence of these alternating minimization schemes, based on the Kurdyka-Łojasiewicz property. In particular, we show that the iterates generated by the alternating minimization scheme, converges to a critical point of this nonconvex nonsmooth objective function. We also extend the analysis to nonconvex nonsmooth regularization model with box constraints, and obtain similar convergence results of the related minimization algorithm. Numerical examples are given to illustrate our convergence analysis. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | Kurdykalojasiewicz inequality | - |
dc.subject | nonconvex and nonsmooth | - |
dc.subject | alternating minimization methods | - |
dc.subject | box-constraints | - |
dc.subject | Image restoration | - |
dc.title | On the convergence of nonconvex minimization methods for image recovery | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2015.2401430 | - |
dc.identifier.scopus | eid_2-s2.0-84925114263 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1587 | - |
dc.identifier.epage | 1598 | - |
dc.identifier.isi | WOS:000351463700004 | - |
dc.identifier.issnl | 1057-7149 | - |