Neural Vector Tomography for Reconstructing a Magnetization Vector Field
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912154444103680 |
|---|---|
| author | Butbaia, Giorgi Zang, Jiadong |
| author_facet | Butbaia, Giorgi Zang, Jiadong |
| contents | Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In this work, we instead model the underlying vector fields using smooth neural fields. Owing to the fact that the activation functions in the neural network may be chosen to be smooth and the domain is no longer pixelated, the model results in high-quality reconstructions, even under presence of noise. In the case where we have underlying global continuous symmetry, we find that the neural network substantially improves the accuracy of the reconstruction over the existing techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09927 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Vector Tomography for Reconstructing a Magnetization Vector Field Butbaia, Giorgi Zang, Jiadong Disordered Systems and Neural Networks Computer Vision and Pattern Recognition Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In this work, we instead model the underlying vector fields using smooth neural fields. Owing to the fact that the activation functions in the neural network may be chosen to be smooth and the domain is no longer pixelated, the model results in high-quality reconstructions, even under presence of noise. In the case where we have underlying global continuous symmetry, we find that the neural network substantially improves the accuracy of the reconstruction over the existing techniques. |
| title | Neural Vector Tomography for Reconstructing a Magnetization Vector Field |
| topic | Disordered Systems and Neural Networks Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.09927 |