Deep learning enhanced quantum holography with undetected photons
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 li...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | PhotoniX |
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| Online Access: | https://doi.org/10.1186/s43074-024-00155-2 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Weiru Fan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-06b986cc5cf847eda55a8fb2d1b3a41b |
| institution | Kabale University |
| issn | 2662-1991 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | PhotoniX |
| spelling | doaj-art-06b986cc5cf847eda55a8fb2d1b3a41b2024-12-22T12:45:49ZengSpringerOpenPhotoniX2662-19912024-12-015111310.1186/s43074-024-00155-2Deep learning enhanced quantum holography with undetected photonsWeiru Fan0Gewei Qian1Yutong Wang2Chen-Ran Xu3Ziyang Chen4Xun Liu5Wei Li6Xu Liu7Feng Liu8Xingqi Xu9Da-Wei Wang10Vladislav V. Yakovlev11Zhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang UniversityZhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang UniversityCollege of Information Science and Electronic Engineering, Zhejiang UniversityZhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang UniversityCollege of Information Science and Engineering, Fujian Key Laboratory of Light Propagation and Transformation, Huaqiao UniversityBeijing Institute of Space and Electricity, China Academy of Space TechnologyBeijing Institute of Space and Electricity, China Academy of Space TechnologyCollege of Optical Science and Engineering, Zhejiang UniversityCollege of Information Science and Electronic Engineering, Zhejiang UniversityZhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang UniversityZhejiang Province Key Laboratory of Quantum Technology and Device, School of Physics, and State Key Laboratory for Extreme Photonics and Instrumentation, Zhejiang UniversityDepartment of Biomedical Engineering, Texas A&M UniversityAbstract 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.https://doi.org/10.1186/s43074-024-00155-2Quantum holographyComputational imagingUndetected photonsDeep learning |
| spellingShingle | 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 Deep learning enhanced quantum holography with undetected photons PhotoniX Quantum holography Computational imaging Undetected photons Deep learning |
| title | Deep learning enhanced quantum holography with undetected photons |
| title_full | Deep learning enhanced quantum holography with undetected photons |
| title_fullStr | Deep learning enhanced quantum holography with undetected photons |
| title_full_unstemmed | Deep learning enhanced quantum holography with undetected photons |
| title_short | Deep learning enhanced quantum holography with undetected photons |
| title_sort | deep learning enhanced quantum holography with undetected photons |
| topic | Quantum holography Computational imaging Undetected photons Deep learning |
| url | https://doi.org/10.1186/s43074-024-00155-2 |
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