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
Date Written: 2005
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: 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