NDLI: Hybrid reconstruction in Bayesian domain

Content Provider	IEEE Xplore Digital Library
Author	Mondal, P.P. Rajan, K. Patnaik, L.M.
Copyright Year	2003
Description	Author affiliation: Dept. of Phys., Indian Inst. of Sci., Bangalore, India (Mondal, P.P.; Rajan, K.)
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.
Sponsorship	IEEE Region 10
Starting Page	1424
Ending Page	1428
File Size	397275
Page Count	5
File Format	PDF
ISBN	0780381629
DOI	10.1109/TENCON.2003.1273153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Image reconstruction Nearest neighbor searches Least squares approximation Reconstruction algorithms Convolution Least squares methods Maximum likelihood estimation Lattices Markov random fields
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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