Quantitative phase microscopy using deep neural networks

Shuai Li; Ayan Sinha; Justin Lee; George Barbastathis

doi:10.1117/12.2289056

23 February 2018 Quantitative phase microscopy using deep neural networks

Shuai Li, Ayan Sinha, Justin Lee, George Barbastathis

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

Abstract

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.

Citation Download Citation

Shuai Li, Ayan Sinha, Justin Lee, and George Barbastathis "Quantitative phase microscopy using deep neural networks", Proc. SPIE 10503, Quantitative Phase Imaging IV, 105032D (23 February 2018); https://doi.org/10.1117/12.2289056

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