Deep learning enhanced quantum holography with undetected photons

Weiru Fan; Gewei Qian; Yutong Wang; Chen-Ran Xu; Ziyang Chen; Xun Liu; Wei Li; Xu Liu; Feng Liu; Xingqi Xu; Da-Wei Wang; Vladislav V. Yakovlev

doi:10.1186/s43074-024-00155-2

Deep learning enhanced quantum holography with undetected photons

doi: 10.1186/s43074-024-00155-2

基金项目:

The National Natural Science Foundation of China (Grant No. 11934011, 62075194, U21A6006). The National Key Research and Development Program of China (Grant No. 2019YFA0308100, 2023YFB2806000, 2022YFA1204700). The Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB28000000). The Open Program of the State Key Laboratory of Advanced Optical Communication Systems and Networks at Shanghai Jiao Tong University (Grant No. 2023GZKF024). The Fundamental Research Funds for the Central Universities. The Information Technology Center and State Key Lab of CAD&

CG.

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出版历程
- 收稿日期: 2024-09-26
- 录用日期: 2024-11-22
- 修回日期: 2024-11-03
- 网络出版日期: 2024-12-18

Deep learning enhanced quantum holography with undetected photons

1.
Zhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
2.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
3.
College of Information Science and Engineering, Fujian Key Laboratory of Light Propagation and Transformation, Huaqiao University, Xiamen, 361021, Fujian Province, China
4.
Beijing Institute of Space and Electricity, China Academy of Space Technology, Beijing, 100094, China
5.
College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
6.
Hefei National Laboratory, Hefei, 230088, Anhui province, China
7.
Department of Biomedical Engineering, Texas A&M University, College Station, 77843, TX, USA

Funds:

CG.

摘要

摘要: Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.
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Abstract: Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.

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