Convolutional Neural Networks Applied to Inline Particle Holography

Three-dimensional particle positioning from inline holograms is performed using convolutional neural networks. The faster R-CNN architecture is implemented for multi-particle identification and lateral positioning, and a second network estimates the depth position. Supervised learning is used to train the network using simulated holograms.