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This research applies deep learning to bypass traditional calibration bottlenecks in fluorescence microscopy, enabling super-resolution imaging from a single camera frame without prior system adjustments. By training on synthetic data that spans diverse optical parameters, the model achieves significant resolution improvements and robustness against noise or aberrations for fast-moving biological samples. While rooted in optics physics, the core innovation lies entirely within machine learning architectures designed to reconstruct nanoscale details efficiently.
Calibration-free single-frame super-resolution fluorescence microscopy
arXiv:2505.13293v4 Announce Type: replace-cross 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 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.
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 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.
Optics (physics.optics); Quantum Physics (quant-ph)
arXiv:2505.13293 [physics.optics]
(or arXiv:2505.13293v4 [physics.optics] for this version)
https://doi.org/10.48550/arXiv.2505.13293 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1002/lpor.71690 Focus to learn more DOI(s) linking to related resources
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