An Information-Theoretic Approach for Image Reconstruction: The Black and White Case

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25TH INTERNATIONAL WORKSHOP, pp. 223-230, K. Knuth, ed., Maxent, 2005

Posted: 14 Feb 2010  

Amos Golan

American University - Department of Economics

Avinash S Bhati

The Urban Institute

Bahattin Buyuksahin

Bank of Canada

Date Written: 2005

Abstract

In this paper an Information–Theoretic method for reconstructing noisy and blurry images is developed. Basically, the inverse problem is transformed into a generalized moment problem, which is then solved by an information theoretic method. This estimation approach is robust for a whole class of distributions and allows the use of prior information. The resulting method builds on the foundations of information-theoretic methods, uses minimal distributional assumptions, performs well and uses efficiently all the available information (hard and soft data). This method is computationally efficient. A number of empirical examples are presented.

Keywords: Entropy, Estimation, Image Reconstruction, Information

Suggested Citation

Golan, Amos and Bhati, Avinash S and Buyuksahin, Bahattin, An Information-Theoretic Approach for Image Reconstruction: The Black and White Case (2005). BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25TH INTERNATIONAL WORKSHOP, pp. 223-230, K. Knuth, ed., Maxent, 2005. Available at SSRN: https://ssrn.com/abstract=1552668

Amos Golan

American University - Department of Economics ( email )

4400 Massachusetts Avenue, N.W.
Washington, DC 20016-8029
United States

Avinash S Bhati

The Urban Institute ( email )

2100 M Street, NW
Washington, DC 20037
United States

Bahattin Buyuksahin (Contact Author)

Bank of Canada ( email )

234 Wellington Street
Ontario, Ottawa K1A 0G9
Canada

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