PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Gwinky G. K. Yip, Michelle C. K. Lo, Kenneth K. Y. Wong, Kevin K. Tsia, "Image restoration of FACED microscopy by generative adversarial network," Proc. SPIE 12390, High-Speed Biomedical Imaging and Spectroscopy VIII, 1239004 (16 March 2023); https://doi.org/10.1117/12.2654550