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

TL;DR

This paper presents a universal, calibration-free deep learning framework for super-resolution imaging that generalizes across diverse optical setups, enabling high-quality reconstructions from single camera frames without prior calibration.

Contribution

The authors introduce a device-agnostic deep learning approach that eliminates the need for calibration data, allowing super-resolution imaging across various systems using simulated training data.

Findings

Achieves superior accuracy and efficiency compared to existing methods.

Successfully generalizes across microscopy, astronomy, and single-molecule imaging datasets.

Enables high-resolution imaging without prior knowledge of optical system parameters.

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 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.