Quantitative phase microscopy using deep neural networks
Author(s)
Li, Shuai
Sinha, Ayan T
Lee, Justin
Barbastathis, George
Date Issued
February 2018
Journal
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
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.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1117/12.2289056
