A Bayesian approach to tomography of multiply scattered beams

Zachary H. Levine

doi:10.1117/12.640627

2 February 2006 A Bayesian approach to tomography of multiply scattered beams

Zachary H. Levine

Proceedings Volume 6065, Computational Imaging IV; 60650V (2006) https://doi.org/10.1117/12.640627
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Recently, Levine, Kearsley, and Hagedorn proposed a generalization of generalized Gaussian random Markov field (GGMRF) as developed by Bouman and Sauer. The principal components of the Bouman-Sauer formulation are a quadratic approximation to the log-likelihood assuming a Poisson distribution and a Beer's Law interaction and a prior distribution which penalized deviation of the values in a neighborhood as a user-defined power in the interval (1-2]. The generalization removes the restriction that the transmission function follows Beer's Law, but instead admits any functional form for the transmission-thickness relation, such as those arising in transmission electron microscopy of thick samples. Several illustrative examples are given in this paper.

Citation Download Citation

Zachary H. Levine "A Bayesian approach to tomography of multiply scattered beams", Proc. SPIE 6065, Computational Imaging IV, 60650V (2 February 2006); https://doi.org/10.1117/12.640627

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