23 February 2018 Quantitative phase microscopy using deep neural networks
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Proceedings Volume 10503, Quantitative Phase Imaging IV; 105032D (2018) https://doi.org/10.1117/12.2289056
Event: SPIE BiOS, 2018, San Francisco, California, United States
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shuai Li, Shuai Li, Ayan Sinha, Ayan Sinha, Justin Lee, Justin Lee, George Barbastathis, George Barbastathis, } "Quantitative phase microscopy using deep neural networks", Proc. SPIE 10503, Quantitative Phase Imaging IV, 105032D (23 February 2018); doi: 10.1117/12.2289056; https://doi.org/10.1117/12.2289056

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