Abstract
The problem of image reconstruction from projections is translated, via approximation of the image by linear combination of basis pictures, into a large system of approximate equalities in the unknown coefficients of the desired linear combination. Optimization criteria for selecting these coefficients include unconstrained regularized least squares estimation based on Bayesian optimization, and constrained norm minimization and entropy maximization. Algebraic reconstruction techniques (ART) are given that converge to these different optimizers. Methods for evaluating the efficacy of reconstruction procedures are discussed and illustrated. Reasons are given for the lack of widespread use of maximum entropy and Bayesian optimization techniques in practical applications of image reconstruction from projections. The ideas are illustrated by a small sample of techniques.
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References
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Herman, G.T. (1985). Application of Maximum Entropy and Bayesian Optimization Methods to Image Reconstruction from Projections. In: Smith, C.R., Grandy, W.T. (eds) Maximum-Entropy and Bayesian Methods in Inverse Problems. Fundamental Theories of Physics, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2221-6_14
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DOI: https://doi.org/10.1007/978-94-017-2221-6_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-8418-7
Online ISBN: 978-94-017-2221-6
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