High-speed three-dimensional digital holographic reconstruction of particles based on a YOLO network
High-speed three-dimensional digital holographic reconstruction of particles based on a YOLO network
复制标题
基于YOLO网络的粒子高速三维数字全息重建
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yufeng Wu
中科院分区：
文献类型：
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作者：
Qiran Shi;Liangcai Cao;Yufeng Wu
提出了一种基于YOLO架构的高速三维数字全息重建算法，能够显著加快训练过程。监督学习被用来训练网络，同时使用模拟全息图和实验全息图。在迁移学习的帮助下，一个小的二维全息图集就足以训练网络。训练后的网络也可以用来标记新的全息图。这些全息图反过来可以帮助训练网络来提高鲁棒性。训练过程需要几个小时，这比之前提出的用几天时间训练相同数据集的网络更有效。该网络对高动态场景具有很大的应用潜力，并且在粒子场重建中对背景噪声具有较强的鲁棒性。
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.