Variational Regularization for Systems of Inverse Problems
Tikhonov Regularization with Multiple Forward Operators
Seiten
2019
|
1st ed. 2019
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-25389-9 (ISBN)
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-25389-9 (ISBN)
Tikhonov regularization is a cornerstone technique in solving inverse problems with applications in countless scientific fields. Richard Huber discusses a multi-parameter Tikhonov approach for systems of inverse problems in order to take advantage of their specific structure. Such an approach allows to choose the regularization weights of each subproblem individually with respect to the corresponding noise levels and degrees of ill-posedness.
Richard Huber wrote his master’s thesis under the supervision of Prof. Dr. Kristian Bredies at the Institute for Mathematics and Scientific Computing at Graz University, Austria.
General Tikhonov Regularization.- Specific Discrepancies.- Regularization Functionals.- Application to STEM Tomography Reconstruction.
| Erscheinungsdatum | 02.03.2019 |
|---|---|
| Reihe/Serie | BestMasters |
| Zusatzinfo | IX, 136 p. 1 illus. |
| Verlagsort | Wiesbaden |
| Sprache | englisch |
| Maße | 148 x 210 mm |
| Gewicht | 201 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Schlagworte | Applied mathematics • Electron Tomography • Ill-Posed Inverse Problems • Kullback-Leibler Discrepancy • Mathematical Image Processing • Radon Transform • Tomography Reconstruction • Total Generalized Variation |
| ISBN-10 | 3-658-25389-4 / 3658253894 |
| ISBN-13 | 978-3-658-25389-9 / 9783658253899 |
| Zustand | Neuware |
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