The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts

Alexandre Goy; Kwabena Arthur; Shuai Li; George Barbastathis

doi:10.1117/12.2513314

4 March 2019 The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts

Alexandre Goy, Kwabena Arthur, Shuai Li, George Barbastathis

Author Affiliations +

Proceedings Volume 10887, Quantitative Phase Imaging V; 108870S (2019) https://doi.org/10.1117/12.2513314
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

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.

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Alexandre Goy, Kwabena Arthur, Shuai Li, and George Barbastathis "The importance of physical pre-processors for quantitative phase retrieval under extremely low photon counts", Proc. SPIE 10887, Quantitative Phase Imaging V, 108870S (4 March 2019); https://doi.org/10.1117/12.2513314

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