Quantitative phase microscopy using deep neural networks
Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data gener...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
SPIE
2018
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| Online Access: | http://hdl.handle.net/1721.1/119144 https://orcid.org/0000-0002-7836-0431 https://orcid.org/0000-0002-4140-1404 |
| Summary: | Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved. |
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