End-to-end deep learning for superoscillatory subtraction imaging Article Swipe
Zhigang Dai
,
Zhi Hong
,
Bin Fang
,
Fangzhou Shu
,
Shengtao Mei
,
Zhongwei Jin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.16115
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.16115
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.
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- preprint
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- http://arxiv.org/abs/2511.16115
- https://arxiv.org/pdf/2511.16115
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End-to-end deep learning for superoscillatory subtraction imagingWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-11-20Full publication date if available
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Zhigang Dai, Zhi Hong, Bin Fang, Fangzhou Shu, Shengtao Mei, Zhongwei JinList of authors in order
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https://arxiv.org/abs/2511.16115Publisher landing page
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https://arxiv.org/pdf/2511.16115Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2511.16115Direct OA link when available
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0Total citation count in OpenAlex
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