Tomographic reconstruction from noisy data

被引：0

作者：

Golan, A ^{[1
]}

Dose, V ^{[1
]}

机构：

[1] American Univ, Dept Econ, Washington, DC 20016 USA

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2002年 / 617卷

关键词：

D O I：

暂无

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

A generalized maximum entropy based approach to noisy inverse problems such a's 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.

引用

页码：248 / 258

页数：11

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