Deep Learning Enhanced Quantum Holography with Undetected Photons
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866913616901439488 |
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| author | Fan, Weiru Qian, Gewei Wang, Yutong Xu, Chen-Ran Chen, Ziyang Liu, Xun Li, Wei Liu, Xu Liu, Feng Xu, Xingqi Wang, Da-Wei Yakovlev, Vladislav V. |
| author_facet | Fan, Weiru Qian, Gewei Wang, Yutong Xu, Chen-Ran Chen, Ziyang Liu, Xun Li, Wei Liu, Xu Liu, Feng Xu, Xingqi Wang, Da-Wei Yakovlev, Vladislav V. |
| contents | 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Enhanced Quantum Holography with Undetected Photons Fan, Weiru Qian, Gewei Wang, Yutong Xu, Chen-Ran Chen, Ziyang Liu, Xun Li, Wei Liu, Xu Liu, Feng Xu, Xingqi Wang, Da-Wei Yakovlev, Vladislav V. Optics 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. |
| title | Deep Learning Enhanced Quantum Holography with Undetected Photons |
| topic | Optics |
| url | https://arxiv.org/abs/2409.18887 |