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1 August 2021 High-speed three-dimensional digital holographic reconstruction of particles based on a YOLO network
Qiran Shi, Liangcai Cao, Yufeng Wu
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180417 (2021) https://doi.org/10.1117/12.2593597
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
A high-speed three-dimensional digital holographic reconstruction algorithm is proposed based on the YOLO architecture, which is able to significantly accelerate the training process. Supervised learning is used to train the network using both simulated and experimental holograms. With the aid of transfer learning, a small set of 2D holograms is sufficient to train the network. The trained network can also be used to label new holograms. These holograms in turn can help train the networks to improve the robustness. It takes hours for the training process, which is more efficient than the previously proposed networks with several days for the same dataset. The network has great potential for high-dynamic scenes and is robust to background noise in the particle field reconstruction.
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Qiran Shi, Liangcai Cao, and Yufeng Wu "High-speed three-dimensional digital holographic reconstruction of particles based on a YOLO network", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180417 (1 August 2021); https://doi.org/10.1117/12.2593597
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KEYWORDS
Digital holography
Holography
Particles
Holograms
Reconstruction algorithms
3D modeling
Machine learning
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