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.
Digital holographic imaging is able to reconstruct 3D or phase information of the object from a one-shot 2D lensless hologram. The inverse reconstruction of 3D particle field could be realized based on the deep convolutional neural network. The hologram of a single particle is spread throughout the detector. Deep convolutional neural network could perform particle feature extraction and obtain the 3D position of each particle. We propose a learning-based approach for 3D holographic particle imaging. A dense encoder-decoder U-net network is designed. Compared with the CNN-based U-net network and the residual connection-based U-net network, the proposed network can reduce the number of network parameters, increase the amount of information of each layer of particles, extract accurate particle characteristics, and improve robustness. The Dense-U-net is more efficient in the way it processes data and requires a less memory storage for the learned model.
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