Holographic Image Reconstruction with Phase Recovery and Autofocusing Using Recurrent Neural Networks

Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information on 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 on 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.