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http://hdl.handle.net/2080/365
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DC Field | Value | Language |
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dc.contributor.author | Sunil Kumar, K | - |
dc.contributor.author | Nanda, P K | - |
dc.contributor.author | Desai, U B | - |
dc.date.accessioned | 2006-11-29T08:49:46Z | - |
dc.date.available | 2006-11-29T08:49:46Z | - |
dc.date.issued | 1997 | - |
dc.identifier.citation | Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications, 2-4 December 1997, Brisbane, P 21-24 | en |
dc.identifier.uri | http://hdl.handle.net/2080/365 | - |
dc.description | Copyright for this article belongs to IEEE | en |
dc.description.abstract | We present a framework based on modular integration and multiresolution for restoring images. We model the image as a Markov random field (MRF) and propose a restoration algorithm. In essence, the problem of image restoration requires learning of the MRF model and noise parameters which are used to restore degraded images. In the developed scheme, there exists interaction between the model learning module and the image restoration module. A method based on homotopy continuation is used for unsupervised model learning and the restoration is achieved through the minimization of an energy function | en |
dc.format.extent | 669737 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | IEEE | en |
dc.subject | Markov processes | en |
dc.subject | image resolution | en |
dc.subject | image restoration | en |
dc.subject | parameter estimation | en |
dc.subject | random noise | en |
dc.subject | unsupervised learning | en |
dc.title | A modular integration and multiresolution framework for image restoration | en |
dc.type | Article | en |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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nandapk1.pdf | 654.04 kB | Adobe PDF | View/Open |
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