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

被引:770
作者
Rivenson, Yair [1 ,2 ,3 ]
Zhang, Yibo [1 ,2 ,3 ]
Gunaydin, Harun [1 ]
Teng, Da [1 ,4 ]
Ozcan, Aydogan [1 ,2 ,3 ,5 ]
机构
[1] Univ Calif Los Angeles, Elect & Comp Engn Dept, Los Angeles, CA 90095 USA
[2] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90095 USA
[3] Univ Calif Los Angeles, CNSI, Los Angeles, CA 90095 USA
[4] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA
[5] Univ Calif Los Angeles, David Geffen Sch Med, Dept Surg, Los Angeles, CA 90095 USA
关键词
deep learning; holography; machine learning; neural networks; phase recovery; LIGHT-EMITTING-DIODES; MOLECULAR-BEAM EPITAXY; PIEZOELECTRIC POLARIZATION; EXTRACTION EFFICIENCY; GALLIUM-NITRIDE; GAN; FIELD; ALN;
D O I
10.1038/lsa.2017.141
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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.
引用
收藏
页码:17141 / 17141
页数:16
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