Journal article
Quantitative Abel tomography robust to noisy, corrupted and missing data
Optimization and engineering, Vol.11(3), pp.381-393
09/2010
Handle:
https://hdl.handle.net/2376/117386
Abstract
A mixed-variable optimization (MVO) approach to quantitative tomography was applied to experimental x-ray data. The results were found to be comparable to previous tests on synthetic data. The MVO method was tested for robustness to realistic data problems: actual radiographic occlusions, simulated amplified noise, and random pixel rejection. Significant levels of data corruption, which easily render inverse methods ineffective or unreliable, do not noticeably impact the MVO method reliability. The success of the MVO method lies in its use of a reduced dimension object description designed to capture prior knowledge about the class of potential imaged objects.
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Details
- Title
- Quantitative Abel tomography robust to noisy, corrupted and missing data
- Creators
- Thomas Asaki - Department of Mathematics Washington State University Neill Hall 103 P.O. Box 643113 Pullman WA 99164-3113 USA
- Publication Details
- Optimization and engineering, Vol.11(3), pp.381-393
- Academic Unit
- Mathematics and Statistics, Department of
- Publisher
- Springer US; Boston
- Identifiers
- 99900548350301842
- Language
- English
- Resource Type
- Journal article