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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22460 |
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| _version_ | 1866908288512163840 |
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| author | Yan, YongKang Gan, Zeqian Hu, Luying Xu, Xinrui Kang, Ran Qian, Chengwei Mei, Jianqiang Beckett, Paul Shieh, William Yin, Rui He, Xin Liu, Xu |
| author_facet | Yan, YongKang Gan, Zeqian Hu, Luying Xu, Xinrui Kang, Ran Qian, Chengwei Mei, Jianqiang Beckett, Paul Shieh, William Yin, Rui He, Xin Liu, Xu |
| contents | High-dimensional imaging technology has demonstrated significant research value across diverse fields, including environmental monitoring, agricultural inspection, and biomedical imaging, through integrating spatial (X*Y), spectral, and polarization detection functionalities. Here, we report a High-Dimensional encoding computational imaging technique, utilizing 4 high-dimensional encoders (HDE1-4) and a high-dimensional neural network (HDNN) to reconstruct 80 high-dimensional images of the target. The system efficiently acquires spectral-polarization information, spanning a wavelength range of 400-800 nm at intervals of 20 nm, obtaining 20 spectral datasets. Each dataset contains images captured at 4 polarization angles (0°, 45°, 90°, and -45°), and the image resolution can reach up to 1280 * 960 pixels. Achieving a reconstruction ratio 1:20. Experimental validation confirms that the spectral reconstruction error consistently remains below 0.14%. Extensive high-dimensional imaging experiments were conducted under indoor and outdoor conditions, showing the system's significant adaptability and robustness in various environments. Compared to traditional imaging devices, such as hyperspectral cameras that could only acquire spectral information, while polarization cameras are limited to polarization imaging, this integrated system successfully overcomes these technological constraints, providing an innovative and efficient solution for high-dimensional optical sensing applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Encoding Computational Imaging Yan, YongKang Gan, Zeqian Hu, Luying Xu, Xinrui Kang, Ran Qian, Chengwei Mei, Jianqiang Beckett, Paul Shieh, William Yin, Rui He, Xin Liu, Xu Optics High-dimensional imaging technology has demonstrated significant research value across diverse fields, including environmental monitoring, agricultural inspection, and biomedical imaging, through integrating spatial (X*Y), spectral, and polarization detection functionalities. Here, we report a High-Dimensional encoding computational imaging technique, utilizing 4 high-dimensional encoders (HDE1-4) and a high-dimensional neural network (HDNN) to reconstruct 80 high-dimensional images of the target. The system efficiently acquires spectral-polarization information, spanning a wavelength range of 400-800 nm at intervals of 20 nm, obtaining 20 spectral datasets. Each dataset contains images captured at 4 polarization angles (0°, 45°, 90°, and -45°), and the image resolution can reach up to 1280 * 960 pixels. Achieving a reconstruction ratio 1:20. Experimental validation confirms that the spectral reconstruction error consistently remains below 0.14%. Extensive high-dimensional imaging experiments were conducted under indoor and outdoor conditions, showing the system's significant adaptability and robustness in various environments. Compared to traditional imaging devices, such as hyperspectral cameras that could only acquire spectral information, while polarization cameras are limited to polarization imaging, this integrated system successfully overcomes these technological constraints, providing an innovative and efficient solution for high-dimensional optical sensing applications. |
| title | High-Dimensional Encoding Computational Imaging |
| topic | Optics |
| url | https://arxiv.org/abs/2503.22460 |