Papers › Calibration-free single-frame super-resolution fluorescence microscopy
Calibration-free single-frame super-resolution fluorescence microscopy
Anežka Dostálová, Dominik Vašinka, Robert Stárek, Miroslav Ježek
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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 and ThunderSTORM multi-emitter fitting. The results confirm the ability to resolve emitters separated by 35 nm at 580 nm wavelength, corresponding to sevenfold 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 prior information and calibration, our approach enables plug-and-play super-resolution imaging of fast, dense, or light-sensitive samples on standard wide-field setups.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections