Enhancing terahertz imaging with Rydberg atom-based sensors using untrained neural networks
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作者
Wan, Jun; Zhang, Bin; Li, Xianzhe; Li, Tao; Huang, Qirong; Yang, Xinyu; Zhang, Kaiqing; Huang, Wei; Deng, Haixiao
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刊物名称
CHINESE OPTICS LETTERS
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年、卷、文献号
2025, 7,
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关键词
Wan, Jun; Zhang, Bin; Li, Xianzhe; Li, Tao; Huang, Qirong; Yang, Xinyu; Zhang, Kaiqing; Huang, Wei; Deng, Haixiao
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摘要
Terahertz (THz) imaging based on the Rydberg atom achieves high sensitivity and frame rates but faces challenges in spatial resolution due to diffraction, interference, and background noise. This study introduces a polarization filter and a deep learning-based method using a physically informed convolutional neural network to enhance resolution without pre-trained datasets. The technique reduces diffraction artifacts and achieves lens-free imaging with a resolution exceeding 1.25 lp/mm over a wide field of view. This advancement significantly improves the imaging quality of the Rydberg atom-based sensor, expanding its potential applications in THz imaging.
