  Digital holography is one of the most widely used label-free microscopy
techniques in biomedical imaging. Recovery of the missing phase information of
a hologram is an important step in holographic image reconstruction. Here we
demonstrate a convolutional recurrent neural network (RNN) based phase recovery
approach that uses multiple holograms, captured at different sample-to-sensor
distances to rapidly reconstruct the phase and amplitude information of a
sample, while also performing autofocusing through the same network. We
demonstrated the success of this deep learning-enabled holography method by
imaging microscopic features of human tissue samples and Papanicolaou (Pap)
smears. These results constitute the first demonstration of the use of
recurrent neural networks for holographic imaging and phase recovery, and
compared with existing methods, the presented approach improves the
reconstructed image quality, while also increasing the depth-of-field and
inference speed.
