Learning to synthesize: robust phase retrieval at low photon counts
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s41377-020-0267-2.pdf
Description
Published version
Size
3.39 MB
Format
Adobe PDF
Checksum (MD5)
4c8a09de3b772b448da607a7d82d5e5c
Author(s)
Deng, Mo
Li, Shuai
Goy, Alexandre
Kang, Iksung
Barbastathis, George
Date Issued
March 2020
Journal
Light: Science and Applications
Publisher
Springer Science and Business Media LLC
Citation
Deng, Mo et al., "Learning to synthesize: robust phase retrieval at low photon counts." Light: Science and Applications 9 (March 2020): no. 36 doi. 10.1038/s41377-020-0267-2 ©2020
Version
Final published version
Abstract
The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase. Particularly in the case of quantitative phase retrieval, spatial frequencies that are underrepresented in the training database, most often at the high band, tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed, such as pre-amplifying the high spatial frequencies in the examples; however, while that strategy improves the resolution, it also leads to high-frequency artefacts, as well as low-frequency distortions in the reconstructions. Here, we present a new approach that learns separately how to handle the two frequency bands, low and high, and learns how to synthesize these two bands into full-band reconstructions. We show that this “learning to synthesize” (LS) method yields phase reconstructions of high spatial resolution and without artefacts and that it is resilient to high-noise conditions, e.g., in the case of very low photon flux. In addition to the problem of quantitative phase retrieval, the LS method is applicable, in principle, to any inverse problem where the forward operator treats different frequency bands unevenly, i.e., is ill-posed. ©2020, The Author(s)
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41377-020-0267-2
