Virtual-scanning network for high-resolution light-field microscopy

Zhi Lu; Jiamin Wu; Qionghai Dai

doi:10.1117/12.2687464

6 November 2023 Virtual-scanning network for high-resolution light-field microscopy

Zhi Lu, Jiamin Wu, Qionghai Dai

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 1292109 (2023) https://doi.org/10.1117/12.2687464
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

Investigating sophisticated cellular and intercellular behaviors in animals is crucial to biological research, which calls for an intravital high-precision recording at ultrahigh spatiotemporal resolution. Light-Field Microscopy (LFM) achieves snapshot 3D imaging with a microlens array to uncouple the angular information, but at the cost of low spatial resolution. Recently, deep learning has revolved various microscopes including LFM with enhanced capabilities. However, deep learning-based LFM has limited performance in resolution, robustness and generalization ability. To address such challenges and expand the application boundaries of LFM-based technologies, we propose a learning-based framework, termed Virtual-scanning Network (Vs-Net) for light-field microscopy to achieve snapshot subcellular observations in vivo.

Citation Download Citation

Zhi Lu, Jiamin Wu, and Qionghai Dai "Virtual-scanning network for high-resolution light-field microscopy", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 1292109 (6 November 2023); https://doi.org/10.1117/12.2687464

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