Phase recovery from intensity-only measurements forms the heart of coherent
imaging techniques and holography. Here we demonstrate that a neural network
can learn to perform phase recovery and holographic image reconstruction after
appropriate training. This deep learning-based approach provides an entirely
new framework to conduct holographic imaging by rapidly eliminating twin-image
and self-interference related spatial artifacts. Compared to existing
approaches, this neural network based method is significantly faster to
compute, and reconstructs improved phase and amplitude images of the objects
using only one hologram, i.e., requires less number of measurements in addition
to being computationally faster. We validated this method by reconstructing
phase and amplitude images of various samples, including blood and Pap smears,
and tissue sections. These results are broadly applicable to any phase recovery
problem, and highlight that through machine learning challenging problems in
imaging science can be overcome, providing new avenues to design powerful
computational imaging systems.

本文探讨利用神经网络进行相位恢复及全息图像重建，可以消除双幅像和自干涉产生的空间伪影，相较于现有的方法更快、准确。通过计算血液、宫颈涂片、组织切片等样本，验证了该方法的可行性，表明机器学习可以克服成像科学中的挑战性问题，为设计强大的计算成像系统提供了新的思路。

利用深度学习进行神经网络中的相位恢复和全息图像重建

Phase recovery and holographic image reconstruction using deep learning  in neural networks

Deep learning has been proven to yield reliably generalizable answers to
numerous classification and decision tasks. Here, we demonstrate for the first
time, to our knowledge, that deep neural networks (DNNs) can be trained to
solve inverse problems in computational imaging. We experimentally demonstrate
a lens-less imaging system where a DNN was trained to recover a phase object
given a raw intensity image recorded some distance away.

本文首次证明了深度神经网络可用于计算成像中的反问题，并通过训练 DNN 成功地在无镜成像系统中恢复出相位物体。