Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks

Abstract

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.

arXiv comment18 Pages, 7 Figures, 1 Table

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• Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
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• Deep learning phase recovery: data-driven, physics-driven, or combining both?
• Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
• eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
• Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
• Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
• Computer-free, all-optical reconstruction of holograms using diffractive networks