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

Posted on 09.06.2021 - 17:56
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 are 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 provides 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.


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Zhang, Yuhe; Noack, Mike; Vagovic, Patrik; Fezzaa, Kamel; García-Moreno, Francisco; Ritschel, Tobias; et al. (2021): PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasets. The Optical Society. Collection.



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Yuhe Zhang
Mike Noack
Patrik Vagovic
Kamel Fezzaa
Francisco García-Moreno
Tobias Ritschel
Pablo Villanueva-Perez
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