| Autor: |
Brito Carcaño Jesús E., Cuenat Stéphane, Ahmad Belal, Sandoz Patrick, Couturier Raphaël, Laurent Guillaume, Jacquot Maxime |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 287, p 13011 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202328713011 |
| Popis: |
Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments. Different deep architectures such as convolutional neural networks or Vision Transformers can be used in advanced coherent imaging techniques such as digital holography to extract various metrics such as autofocusing reconstruction distance or 3D position determination in order to target automated microscopy or real-time phase image restitution. Deep neural networks can be trained with both datasets simulated and experimental holograms, by transfer learning. Overall, the application of deep neural networks in digital holographic microscopy and 3D computer micro-vision has the potential to significantly improve the robustness and processing speed of holograms to infer and control a 3D position for applications in micro-robotics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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