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Hybrid Reconstruction in Bayesian Domain

Mondal, Partha P and Rajan, K and Patnaik, LM (2003) Hybrid Reconstruction in Bayesian Domain. In: Conference on Convergent Technologies for Asia-Pacific Region TENCON 2003, 15-17 October, Bangalore,India, Vol.4, 1424 -1428.

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Abstract

Image reconstruction in Bayesian framework is far more advantageous over other reconstruction methods like convolution back projection, weighted least square method and maximum likelihood estimation. The power of Bayesian estimation ties in its ability to incorporate the prior distribution knowledge, enabling better reconstruction. Proper specification of clique potentials in Bayesian estimation plays a crucial role in the reconstruction process by favors the presence of desired characteristics in the image lattice like nearest neighbor interactions and homogeneity. Homogenous Markov random fields have been successfully used for modeling such interactions. Though reconstructions produced by such models are far more efficient, they often require large iterations for producing an approximate reconstruction. To deal with this problem, we have extended the Bayesian estimation in order to support sharp reconstruction. We propose to use sharp potential in Bayesian estimation once an approximate reconstruction is available using homogenous potentials in Bayesian domain The advantage of the proposed potential is its ability to recognize correlated nearest neighbors. The proposed reconstruction is a hybrid of both smooth and sharp potential in Bayesian framework and hence it is termed as hybrid reconstruction. Simulated experiments have shown that the proposed hybrid estimation method produces superior and sharp reconstruction as compared to the reconstruction produced by other Bayesian estimation methods.

Item Type: Conference Paper
Additional Information: �©1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 22 Dec 2005
Last Modified: 19 Sep 2010 04:22
URI: http://eprints.iisc.ernet.in/id/eprint/4728

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