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PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets

Journal Article · · Optics Express
DOI:https://doi.org/10.1364/oe.423222· OSTI ID:1839044
 [1];  [2];  [3];  [4];  [5];  [6];  [1]
  1. Lund Univ. (Sweden)
  2. Technische Univ. Berlin (Germany)
  3. Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
  4. Argonne National Lab. (ANL), Lemont, IL (United States)
  5. Helmholtz-Zentrum Berlin fur Materialien und Energie (Germany)
  6. Univ. College London (United Kingdom)
+ Show Author Affiliations
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase information from an intensity hologram or diffraction pattern in a robust manner and in real-time. However, current DL architectures applied to the phase problem rely on i) paired datasets, i. e., they arc only applicable when a satisfactory solution of the phase problem has been found, and ii) the fact that most of them ignore the physics of the imaging process. Here, we present PhaseGAN, a new DL approach based on Generative Adversarial Networks, which allows the use of unpaired datasets and includes the physics of image formation. The performance of our approach is enhanced by including the image formation physics and a novel Fourier loss function, providing phase reconstructions when conventional phase retrieval algorithms fail, such as ultra-fast experiments. Thus, PhaseGAN offers the opportunity to address the phase problem in real-time when no phase reconstructions but good simulations or data from other experiments are available.
Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Bundesministerium für Bildung und Forschung (BMBF); NVIDIA; USDOE Office of Science (SC)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1839044
Journal Information:
Optics Express, Journal Name: Optics Express Journal Issue: 13 Vol. 29; ISSN 1094-4087
Publisher:
Optical Society of America (OSA)Copyright Statement
Country of Publication:
United States
Language:
English

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