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

Title: A modular integration and multiresolution framework for image restoration
Authors: Sunil Kumar, K
Nanda, P K
Desai, U B
Keywords: Markov processes
image resolution
image restoration
parameter estimation
random noise
unsupervised learning
Issue Date: 1997
Publisher: IEEE
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
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
Description: Copyright for this article belongs to IEEE
URI: http://hdl.handle.net/2080/365
Appears in Collections:Conference Papers

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