2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:MA-L4.1
Session:Image and Video Restoration
Time:Monday, September 17, 09:50 - 10:10
Presentation: Lecture
Title: TOTAL VARIATION IMAGE RESTORATION AND PARAMETER ESTIMATION USING VARIATIONAL POSTERIOR DISTRIBUTION APPROXIMATION
Authors: S. Derin Babacan; Northwestern University 
 Rafael Molina; Universidad de Granada 
 Aggelos K Katsaggelos; Northwestern University 
Abstract: In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following the hierarchical Bayesian framework, we simultaneously estimate the reconstructed image and the unknown hyperparameters for both the image prior and the image degradation noise. Our algorithms provide an approximation to the posterior distributions of the unknowns so that both the uncertainty of the estimates can be measured and different values from these distributions can be used for the estimates. We also show that some of the current approaches to TV-based image restoration are special cases of our variational framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included.



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