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...
Saved in:
| Published in | Bayesian Inference and and Maximum Entropy Methods in Science and Engineering Vol. 617; pp. 248 - 258 |
|---|---|
| Main Authors | , |
| Format | Conference Proceeding |
| Language | English |
| Published |
01.01.2002
|
| Online Access | Get full text |
| ISBN | 0735400636 9780735400634 |
| ISSN | 0094-243X |
Cover
| Summary: | 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. |
|---|---|
| Bibliography: | ObjectType-Conference Proceeding-1 SourceType-Conference Papers & Proceedings-1 content type line 25 |
| ISBN: | 0735400636 9780735400634 |
| ISSN: | 0094-243X |