From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908903616282624 |
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| author | Vašinka, Dominik Juráň, Filip Běhal, Jaromír Ježek, Miroslav |
| author_facet | Vašinka, Dominik Juráň, Filip Běhal, Jaromír Ježek, Miroslav |
| contents | Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment. We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters. Our device-agnostic modeling utilizes diverse, numerically simulated dataset encompassing a broad range of imaging conditions, enabling generalization across different optical setups. Once trained, the model reconstructs super-resolved images directly from a single resolution-limited camera frame with superior accuracy and computational efficiency compared to state-of-the-art methods. We experimentally validate our approach using a custom microscopy setup with controllable ground-truth emitter positions. We also demonstrate its versatility on astronomy and single-molecule localization microscopy datasets, achieving unprecedented resolution without prior information. Our findings establish a pathway toward universal, calibration-free super-resolution imaging, expanding its applicability across scientific disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging Vašinka, Dominik Juráň, Filip Běhal, Jaromír Ježek, Miroslav Optics Instrumentation and Methods for Astrophysics Quantum Physics Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment. We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters. Our device-agnostic modeling utilizes diverse, numerically simulated dataset encompassing a broad range of imaging conditions, enabling generalization across different optical setups. Once trained, the model reconstructs super-resolved images directly from a single resolution-limited camera frame with superior accuracy and computational efficiency compared to state-of-the-art methods. We experimentally validate our approach using a custom microscopy setup with controllable ground-truth emitter positions. We also demonstrate its versatility on astronomy and single-molecule localization microscopy datasets, achieving unprecedented resolution without prior information. Our findings establish a pathway toward universal, calibration-free super-resolution imaging, expanding its applicability across scientific disciplines. |
| title | From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging |
| topic | Optics Instrumentation and Methods for Astrophysics Quantum Physics |
| url | https://arxiv.org/abs/2502.18637 |