基于递归神经网络的相位恢复和自聚焦全息图像重建
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
摘要：
数字全息是生物医学成像中应用最广泛的无标记显微技术之一。全息图丢失相位信息的恢复是全息图像重建的重要环节。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，该方法使用在不同的样本到传感器距离处捕获的多个全息图来快速重建样本的相位和振幅信息，同时还通过相同的网络执行自动聚焦。我们通过对人体组织样本和巴氏涂片（Pap）的显微特征进行成像，证明了这种支持深度学习的全息方法的成功。这些结果构成了使用递归神经网络进行全息成像和相位恢复的第一个演示，与现有的方法相比，所提出的方法提高了重建图像的质量，同时也增加了景深和推理速度。
英文摘要：
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.
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