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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/365

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contributor.authorSunil Kumar, K-
contributor.authorNanda, P K-
contributor.authorDesai, U B-
date.accessioned2006-11-29T08:49:46Z-
date.available2006-11-29T08:49:46Z-
date.issued1997-
identifier.citationProceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications, 2-4 December 1997, Brisbane, P 21-24en
identifier.urihttp://hdl.handle.net/2080/365-
descriptionCopyright for this article belongs to IEEEen
description.abstractWe 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 functionen
format.extent669737 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectMarkov processesen
subjectimage resolutionen
subjectimage restorationen
subjectparameter estimationen
subjectrandom noiseen
subjectunsupervised learningen
titleA modular integration and multiresolution framework for image restorationen
typeArticleen
Appears in Collections:Conference Papers

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