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.


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    Titel :

    Image restoration of FACED microscopy by generative adversarial network


    Beteiligte:

    Kongress:

    High-Speed Biomedical Imaging and Spectroscopy VIII ; 2023 ; San Francisco,California,United States


    Erschienen in:

    Proc. SPIE ; 12390


    Erscheinungsdatum :

    2023-03-16





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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