We report the use of conditional generative adversarial network (cGAN) for restoring undersampled images captured in free-space angular-chirp-enhanced delay (FACED) microscopy. We show that this deep-learning approach allows the wider imaging field of view (FOV) along FACED axis, without substantially sacrificing the imaging resolution, photon-budget and speed even with lower density of scanning foci. This study could show the potential of further extending the applicability of FACED imaging to a wider range of biological applications that require extended FOV imaging.
Image restoration of FACED microscopy by generative adversarial network
High-Speed Biomedical Imaging and Spectroscopy VIII ; 2023 ; San Francisco,California,United States
Proc. SPIE ; 12390
2023-03-16
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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