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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17694 |
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| _version_ | 1866917221290213376 |
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| author | Zhao, Qianhao Hong, Zhixuan Wang, Ruihai Wang, Tianbo Jiang, Lingzhi Ma, Qiong Lu, Peng-Han Dunin-Borkowski, Rafal E. Maiden, Andrew Zheng, Guoan |
| author_facet | Zhao, Qianhao Hong, Zhixuan Wang, Ruihai Wang, Tianbo Jiang, Lingzhi Ma, Qiong Lu, Peng-Han Dunin-Borkowski, Rafal E. Maiden, Andrew Zheng, Guoan |
| contents | Ptychography spans from sub-angstrom to meter scales yet suffers from convergence instability and excessive data redundancy. Here we introduce self-correcting residual neural fields as a dose-efficient framework for electron, X-ray, and optical ptychography. Unlike approaches that split complex fields, our complex-valued architecture employs holomorphic phasor activation e^iωz to preserve intrinsic phase-amplitude coupling. We reformulate reconstruction as residual learning, where the network learns only corrections to physical priors rather than complete wavefields. By embedding the physical model as a differentiable layer within the network, we enable end-to-end automatic differentiation where experimental parameters are jointly corrected alongside the neural fields. We validate our scheme across conventional, near-field, coded, and Fourier ptychography and achieve record-breaking lensless resolution of 244-nm linewidth with visible light. Extending to electron wavelengths, we reveal synaptic connectivity in brain sections with superior performance over conventional approaches. Our framework provides a solution for high-throughput, dose-efficient nanoscopy across the electromagnetic spectrum. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17694 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Residual neural-field ptychography for dose-efficient electron, X-ray, and optical nanoscopy Zhao, Qianhao Hong, Zhixuan Wang, Ruihai Wang, Tianbo Jiang, Lingzhi Ma, Qiong Lu, Peng-Han Dunin-Borkowski, Rafal E. Maiden, Andrew Zheng, Guoan Optics Computational Physics Ptychography spans from sub-angstrom to meter scales yet suffers from convergence instability and excessive data redundancy. Here we introduce self-correcting residual neural fields as a dose-efficient framework for electron, X-ray, and optical ptychography. Unlike approaches that split complex fields, our complex-valued architecture employs holomorphic phasor activation e^iωz to preserve intrinsic phase-amplitude coupling. We reformulate reconstruction as residual learning, where the network learns only corrections to physical priors rather than complete wavefields. By embedding the physical model as a differentiable layer within the network, we enable end-to-end automatic differentiation where experimental parameters are jointly corrected alongside the neural fields. We validate our scheme across conventional, near-field, coded, and Fourier ptychography and achieve record-breaking lensless resolution of 244-nm linewidth with visible light. Extending to electron wavelengths, we reveal synaptic connectivity in brain sections with superior performance over conventional approaches. Our framework provides a solution for high-throughput, dose-efficient nanoscopy across the electromagnetic spectrum. |
| title | Residual neural-field ptychography for dose-efficient electron, X-ray, and optical nanoscopy |
| topic | Optics Computational Physics |
| url | https://arxiv.org/abs/2601.17694 |