Speaker
Description
Any optical imaging system, from microscopes to telescopes, is subjected to diffraction owing to its finite aperture. For objects composed of many single emitters, such as molecules, quantum dots, or stars, diffraction blurring can significantly impair our ability to determine the number and positions of emitters present. Deep learning techniques showed great potential in overcoming this problem. However, these methods remain device-specific, requiring explicit knowledge of the imaging system and its parameters.
We propose a novel device-agnostic deep learning approach that surpasses these limitations. In this work, we demonstrate the generalization ability of the device-agnostic modeling on an unprecedented scale. From optical laboratory systems and dense-molecule microscopy to galaxy observations, our approach uses no information about the imaging system while achieving better accuracy than established methods.
Using a single device-agnostic model, without retraining or calibration, we reconstruct high-resolution images across numerous applications. Namely, we applied it to stacks of single-molecule localization microscopy data, revealing tubulin structures and nuclear pore complexes in great detail. Additionally, we verify the super-resolution ability by reconstructing fully unresolved binary stars from ground-based telescope data and comparing the results with those from a space observatory. Finally, we analyze generalization across numerous simulated imaging systems with widely varying parameters, such as signal-to-noise ratio, emitter density, and point-spread function profiles, including aberrations.