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dc.contributor.authorSunil Kumar, K-
dc.contributor.authorNanda, P K-
dc.contributor.authorDesai, U B-
dc.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
dc.descriptionCopyright for this article belongs to IEEEen
dc.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
dc.format.extent669737 bytes-
dc.subjectMarkov processesen
dc.subjectimage resolutionen
dc.subjectimage restorationen
dc.subjectparameter estimationen
dc.subjectrandom noiseen
dc.subjectunsupervised learningen
dc.titleA modular integration and multiresolution framework for image restorationen
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