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.
@article{arxiv.2511.16115,
title = {End-to-end deep learning for superoscillatory subtraction imaging},
author = {Zhongwei Jin and Keyi Chen and Qiuyu Ren and Zhigang Dai and Ruoping Yao and Zhi Hong and Bin Fang and Fangzhou Shu and Shengtao Mei and Yiping Lu},
journal= {arXiv preprint arXiv:2511.16115},
year = {2026}
}