Self-supervised prior learning improves structured illumination microscopy resolution
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911298728493056 |
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| author | Wang, Ze-Hao Weng, Tong-Tian Shan, Long-Kun Chen, Xiang-Dong Guo, Guang-Can Sun, Fang-Wen Chen, Tian-Long |
| author_facet | Wang, Ze-Hao Weng, Tong-Tian Shan, Long-Kun Chen, Xiang-Dong Guo, Guang-Can Sun, Fang-Wen Chen, Tian-Long |
| contents | Structured illumination microscopy (SIM) is a wide-field super-resolution technique normally limited to roughly twice the diffraction-limited resolution ($\approx 100$--$200$~nm). Surpassing this bound is a classic ill-posed inverse problem: recovering high-frequency structure from band-limited raw data. We introduce SIMFormer, a fully blind SIM reconstruction framework that learns a powerful, data-driven prior directly from raw images via self-supervision. This learned prior regularizes the solution and enables reliable extrapolation beyond the optical transfer function cutoff, yielding an effective resolution of approximately 45~nm. We validate SIMFormer on synthetic data and the BioSR dataset, where it resolves features such as flattened endoplasmic reticulum lipid bilayers previously reported to require STORM-level resolution. A self-distilled variant, SIMFormer+, further improves noise robustness while preserving high resolution at extremely low photon counts. These results show that learned priors can substantially extend SIM resolution and robustness, enabling rapid, large-scale imaging with STORM-level detail. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-supervised prior learning improves structured illumination microscopy resolution Wang, Ze-Hao Weng, Tong-Tian Shan, Long-Kun Chen, Xiang-Dong Guo, Guang-Can Sun, Fang-Wen Chen, Tian-Long Optics Structured illumination microscopy (SIM) is a wide-field super-resolution technique normally limited to roughly twice the diffraction-limited resolution ($\approx 100$--$200$~nm). Surpassing this bound is a classic ill-posed inverse problem: recovering high-frequency structure from band-limited raw data. We introduce SIMFormer, a fully blind SIM reconstruction framework that learns a powerful, data-driven prior directly from raw images via self-supervision. This learned prior regularizes the solution and enables reliable extrapolation beyond the optical transfer function cutoff, yielding an effective resolution of approximately 45~nm. We validate SIMFormer on synthetic data and the BioSR dataset, where it resolves features such as flattened endoplasmic reticulum lipid bilayers previously reported to require STORM-level resolution. A self-distilled variant, SIMFormer+, further improves noise robustness while preserving high resolution at extremely low photon counts. These results show that learned priors can substantially extend SIM resolution and robustness, enabling rapid, large-scale imaging with STORM-level detail. |
| title | Self-supervised prior learning improves structured illumination microscopy resolution |
| topic | Optics |
| url | https://arxiv.org/abs/2511.22053 |