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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/2511.16115 |
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| _version_ | 1866914512103276544 |
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| author | Jin, Zhongwei Chen, Keyi Ren, Qiuyu Dai, Zhigang Yao, Ruoping Hong, Zhi Fang, Bin Shu, Fangzhou Mei, Shengtao Lu, Yiping |
| author_facet | Jin, Zhongwei Chen, Keyi Ren, Qiuyu Dai, Zhigang Yao, Ruoping Hong, Zhi Fang, Bin Shu, Fangzhou Mei, Shengtao Lu, Yiping |
| contents | Breaking the diffraction limit in optical imaging is crucial for resolving subwavelength details in a wide range of applications, where superoscillatory imaging and subtraction imaging are two common strategies for surpassing conventional resolution limits. We propose an end-to-end deep learning framework that integrates superoscillatory focusing and subtraction imaging into a single jointly-optimized vectorial Debye integral neural network pipeline, eliminating the traditional two-step acquisition and manual weighting process. With this end-to-end neural network, we further improve the focusing capability of the system to the sub-100-nm regime, enabling deep-subwavelength imaging resolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16115 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-end deep learning for superoscillatory subtraction imaging Jin, Zhongwei Chen, Keyi Ren, Qiuyu Dai, Zhigang Yao, Ruoping Hong, Zhi Fang, Bin Shu, Fangzhou Mei, Shengtao Lu, Yiping Optics Breaking the diffraction limit in optical imaging is crucial for resolving subwavelength details in a wide range of applications, where superoscillatory imaging and subtraction imaging are two common strategies for surpassing conventional resolution limits. We propose an end-to-end deep learning framework that integrates superoscillatory focusing and subtraction imaging into a single jointly-optimized vectorial Debye integral neural network pipeline, eliminating the traditional two-step acquisition and manual weighting process. With this end-to-end neural network, we further improve the focusing capability of the system to the sub-100-nm regime, enabling deep-subwavelength imaging resolution. |
| title | End-to-end deep learning for superoscillatory subtraction imaging |
| topic | Optics |
| url | https://arxiv.org/abs/2511.16115 |