The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts
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108870S.pdf
Description
Published version
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1.4 MB
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Checksum (MD5)
810ed6fcac0cbe0e9ab664d8c127bbee
Author(s)
Goy, Alexandre Sydney Robert
Arthur, Kwabena K.
Li, Shuai
Barbastathis, George
Date Issued
March 4, 2019
Journal
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Publisher
SPIE
Citation
Goy, Alexandre, Arthur, Kwabena, Li, Shuai and Barbastathis, George. 2019. "The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts." Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 10887.
Version
Final published version
Abstract
© 2019 SPIE. In a recent paper [Goy et al., Phys. Rev. Lett. 121, 243902, 2018], we showed that deep neural networks (DNNs) are very efficient solvers for phase retrieval problems, especially when the photon budget is limited. However, the performance of the DNN is strongly conditioned by a preprocessing step that consists in producing a proper initial guess. In this paper, we study the influence of the preprocessing in more details, in particular the choice of the preprocessing operator. We also empirically demonstrate that, for a DenseNet architecture, the performance of the DNN increases with the number of layers up to a point after which it saturates.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
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.
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DOI of Published Version
https://doi.org/10.1117/12.2513314
