Tomographic reconstruction from noisy data

A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a prop...

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Published inBayesian Inference and and Maximum Entropy Methods in Science and Engineering Vol. 617; pp. 248 - 258
Main Authors Golan, A, Dose, V
Format Conference Proceeding
LanguageEnglish
Published 01.01.2002
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ISBN0735400636
9780735400634
ISSN0094-243X

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Abstract A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of both the noise and signal probabilities are maximized subject to the observed data. We use this method for tomographic reconstruction of the soft X-ray emissivity of hot fusion plasma.
AbstractList A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of both the noise and signal probabilities are maximized subject to the observed data. We use this method for tomographic reconstruction of the soft X-ray emissivity of hot fusion plasma.
Author Golan, A
Dose, V
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