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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/2553

Title: Maximum likelihood estimation of blur from multiple observations
Authors: RAJAGOPALAN, AN
CHAUDHURI, S
Issue Date: 1997
Publisher: I E E E, COMPUTER SOC PRESS
Citation: 1997 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I - V: VOL I: PLENARY, EXPERT SUMMARIES, SPECIAL, AUDIO, UNDERWATER ACOUSTICS, VLSI; VOL II: SPEECH PROCESSING; VOL III: SPEECH PROCESSING, DIGITAL SIGNAL PROCESSING; VOL IV: MULTIDIMENSIONAL SIGNAL PROCESSING, NEURAL NETWORKS - VOL V: STATISTICAL SIGNAL AND ARRAY PROCESSING, APPLICATIONS,2577-2580
Abstract: A limitation of the existing maximum likelihood (ML) based methods for blur identification is that the estimate of blur is poor when the blurring is severe. In this paper, we propose an ML-based method for blur identification from multiple observations of a scene. When the relations among the blurring functions of these observations me known, we show that the estimate of blur obtained by using the proposed method is very good. The improvement is particularly significant under severe blurring conditions. With an increase in the number of images, direct computation of the likelihood function, however, becomes difficult as it involves calculating the determinant and the inverse of the cross-correlation matrix. To tackle this problem, we propose an algorithm that computes the likelihood function recursively as more observations are added.
URI: http://dspace.library.iitb.ac.in/xmlui/handle/10054/16297
http://hdl.handle.net/100/2553
ISBN: 0-8186-7920-4
Appears in Collections:Proceedings papers

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