The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts
© 2019 SPIE. In a recent paper [Goy et al., Phys. Rev. Lett. 121, 243902, 2018], we showed that deep neural networks (DNNs) are very efficient solvers for phase retrieval problems, especially when the photon budget is limited. However, the performance of the DNN is strongly conditioned by a preproce...
| Main Authors: | , , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
SPIE,
2021-12-13T16:10:07Z.
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| Subjects: | |
| Online Access: | Get fulltext |