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.

中文翻译：

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使用循环神经网络进行相位恢复和自动对焦的全息图像重建

数字全息术是生物医学成像中使用最广泛的无标记显微技术之一。全息图上丢失的相位信息的恢复是全息图像重建的重要步骤。在这里，我们展示了一种基于卷积递归神经网络 (RNN) 的相位恢复方法，该方法使用多个全息图，在不同的样本到传感器距离处捕获，以快速重建样本的相位和幅度信息，同时还通过同一网络执行自动对焦。我们通过对人体组织样本和巴氏 (Pap) 涂片的微观特征进行成像，证明了这种支持深度学习的全息方法的成功。这些结果首次证明了使用循环神经网络进行全息成像和相位恢复，并与现有方法进行了比较，