| Autor: |
Guo, Ruipeng, Pan, Zhimeng, Taibi, Andrew, Shepherd, Jason, Menon, Rajesh |
| Rok vydání: |
2020 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1364/OL.387496 |
| Popis: |
Computational Cannula Microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable fast, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with field of view of 200$\mu$m (diameter) and resolution of less than 10$\mu$m using a cannula of diameter of only 220$\mu$m. In addition, we show that this approach can also be extended to macro-photography. |
| Databáze: |
arXiv |
| Externí odkaz: |
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