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

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 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 stellar astronomy and single-molecule localization microscopy datasets of point-like sources, achieving high resolution without prior information. Our findings establish a pathway toward universal, calibration-free super-resolution imaging, expanding its applicability across scientific disciplines.

Optics (physics.optics); Instrumentation and Methods for Astrophysics (astro-ph.IM); Quantum Physics (quant-ph)

arXiv:2502.18637 [physics.optics]

(or arXiv:2502.18637v4 [physics.optics] for this version)

https://doi.org/10.1038/s41467-026-75584-7 DOI(s) linking to related resources