Calibration-free single-frame super-resolution fluorescence microscopy

Abstract

Molecular fluorescence microscopy is a leading approach to super-resolution and nanoscale imaging in life and material sciences. However, super-resolution fluorescence microscopy is often bottlenecked by system-specific calibrations and long acquisitions of sparsely blinking molecules. We present a deep-learning approach that reconstructs super-resolved images directly from a single diffraction-limited camera frame. The model is trained exclusively on synthetic data encompassing a wide range of optical and sample parameters, enabling robust generalization across microscopes and experimental conditions. Applied to dense terrylene samples with 150 ms acquisition time, our method significantly reduces reconstruction error compared to Richardson-Lucy deconvolution, ThunderSTORM multi-emitter fitting, and DECODE based on deep learning. The results confirm the ability to resolve emitters separated by 35 nm at 580 nm wavelength, corresponding to seven-fold resolution improvement beyond the Rayleigh criterion. Furthermore, we demonstrate strong generalization ability of the developed model and its resilience across a broad range of noise levels, numerical apertures, and optical aberrations. By delivering unprecedented details from a single short camera exposure without any prior information and calibration, our approach enables plug-and-play super-resolution imaging of fast, dense, or light-sensitive samples on common wide-field microscopy setups.