From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging

• URL: http://arxiv.org/abs/2502.18637v2
• Date: Thu, 20 Mar 2025 17:15:36 GMT
• Title: From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging
• Authors: Dominik Vašinka, Filip Juráň, Jaromír Běhal, Miroslav Ježek,
• Abstract summary: Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies.<n>Existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 model is trained on a diverse, numerically simulated dataset encompassing a broad range of imaging conditions, enabling generalization across different optical setups. Once trained, it 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 ground-truth emitter positions. We also demonstrate its versatility on astronomical 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.

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