NETT Regularization for Compressed Sensing Photoacoustic Tomography
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910753627308032 |
|---|---|
| author | Antholzer, Stephan Schwab, Johannens Bauer-Marschallinger, Johannes Burgholzer, Peter Haltmeier, Markus |
| author_facet | Antholzer, Stephan Schwab, Johannens Bauer-Marschallinger, Johannes Burgholzer, Peter Haltmeier, Markus |
| contents | We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with Deep Neural Networks (2018), arXiv:1803.00092], which is based on a learned regularizer, for the first time to the CS-PAT problem. We propose a network architecture and training strategy for the NETT that we expect to be useful for other inverse problems as well. All algorithms are compared and evaluated on simulated data, and validated using experimental data for two different types of phantoms. The results on the one the hand indicate great potential of deep learning methods, and on the other hand show that significant future work is required to improve their performance on real-word data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_11158 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | NETT Regularization for Compressed Sensing Photoacoustic Tomography Antholzer, Stephan Schwab, Johannens Bauer-Marschallinger, Johannes Burgholzer, Peter Haltmeier, Markus Numerical Analysis We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with Deep Neural Networks (2018), arXiv:1803.00092], which is based on a learned regularizer, for the first time to the CS-PAT problem. We propose a network architecture and training strategy for the NETT that we expect to be useful for other inverse problems as well. All algorithms are compared and evaluated on simulated data, and validated using experimental data for two different types of phantoms. The results on the one the hand indicate great potential of deep learning methods, and on the other hand show that significant future work is required to improve their performance on real-word data. |
| title | NETT Regularization for Compressed Sensing Photoacoustic Tomography |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/1901.11158 |