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Proceedings Paper

A Bayesian approach to tomography of multiply scattered beams
Author(s): Zachary H. Levine
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Paper 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.

Paper Details

Date Published: 2 February 2006
PDF: 8 pages
Proc. SPIE 6065, Computational Imaging IV, 60650V (2 February 2006); doi: 10.1117/12.640627
Show Author Affiliations
Zachary H. Levine, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 6065:
Computational Imaging IV
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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