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

Yair Rivenson; Yibo Zhang; Harun Günayd(i)n; Da Teng; Aydogan Ozcan

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

机译：使用神经网络中的深度学习进行相恢复和全息图像重建

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Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography.In this study,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.This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram,requiring fewer measurements in addition to being computationally faster.We validated this method by reconstructing the phase and amplitude images of various samples,including blood and Pap smears and tissue sections.These results highlight that challenging problems in imaging science can be overcome through machine learning,providing new avenues to design powerful computational imaging systems.

机译：仅强度测量的相恢复是相干成像技术和全息技术的核心。在这项研究中，我们证明了神经网络可以在适当的训练后学习进行相恢复和全息图像重建。这种基于深度学习的方法提供了一个完整的方法通过快速消除双图像和与自我干扰相关的空间伪影进行全息成像的新框架。这种基于神经网络的方法仅使用一个全息图即可快速计算和重建物体的相位和幅值图像，此外还需要更少的测量我们通过重建各种样本（包括血液和巴氏涂片和组织切片）的相位和幅度图像来验证了该方法的有效性。这些结果表明，可以通过机器学习来克服成像科学中的难题，为设计提供新的途径强大的计算成像系统。

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《光：科学与应用（英文版）》 |2018年第2期|52-61|共10页
作者
Yair Rivenson; Yibo Zhang; Harun Günayd(i)n; Da Teng; Aydogan Ozcan;
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作者单位

Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA;

Bioengineering Department, University of California, Los Angeles, CA 90095, USA;

California NanoSystems Institute(CNSI), University of California, Los Angeles, CA 90095, USA;

Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA;

Bioengineering Department, University of California, Los Angeles, CA 90095, USA;

California NanoSystems Institute(CNSI), University of California, Los Angeles, CA 90095, USA;

Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA;

Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA;

Computer Science Department, University of California, Los Angeles, CA 90095, USA;

Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA;

Bioengineering Department, University of California, Los Angeles, CA 90095, USA;

California NanoSystems Institute(CNSI), University of California, Los Angeles, CA 90095, USA;

Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA;

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入库时间 2024-01-27 06:59:15

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

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