Quantitative phase microscopy using deep neural networks
Author(s)
Li, Shuai; Sinha, Ayan T; Lee, Justin; Barbastathis, George
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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.
Date issued
2018-02Department
Institute for Medical Engineering and Science; Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Quantitative Phase Imaging IV
Publisher
SPIE
Citation
Li, Shuai, et al. “Quantitative Phase Microscopy Using Deep Neural Networks.” Quantitative Phase Imaging IV, 27 January, - February 1, 2018, San Francisco, California, edited by Gabriel Popescu and YongKeun Park, SPIE, 2018, p. 84. © SPIE.
Version: Final published version
ISBN
9781510614918
9781510614925