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Maximum likelihood estimation of blur from multiple observations

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Title Maximum likelihood estimation of blur from multiple observations
 
Creator RAJAGOPALAN, AN
CHAUDHURI, S
 
Description 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.
 
Publisher I E E E, COMPUTER SOC PRESS
 
Date 2011-10-27T12:24:03Z
2011-12-15T09:12:15Z
2011-10-27T12:24:03Z
2011-12-15T09:12:15Z
1997
 
Type Proceedings Paper
 
Identifier 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
0-8186-7920-4
http://dspace.library.iitb.ac.in/xmlui/handle/10054/16297
http://hdl.handle.net/100/2553
 
Source 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 97),MUNICH, GERMANY,APR 21-24, 1997
 
Language English