Abstract
In this thesis the Bayesian modeling and discretization are studied in inverse problems related to imaging. The treatise consists of four articles which focus on the phenomena that appear when more detailed data or a priori information become available. Novel Bayesian methods for solving ill-posed signal processing problems in edge-preserving manner are introduced and analysed. Furthermore, modeling photographs in image processing problems is studied and a novel model is presented.
Translated title of the contribution | Discretization and Bayesian modeling in inverse problems and imaging |
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Original language | English |
Qualification | Doctor's degree |
Awarding Institution |
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Supervisors/Advisors |
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Publisher | |
Print ISBNs | 978-952-60-3040-1 |
Electronic ISBNs | 978-952-60-3041-8 |
Publication status | Published - 2010 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- inverse problems
- Mumford-Shah functional
- Bayesian inversion
- hierarchical modeling
- discretization invariance
- image model
- Borel measure
- metric space