Universal Computational Super-Resolution Framework for Imaging of Quantum Dots

We present a universal deep learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In-(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe₂, resolving overlapping emitters even under low signal-to-noise ratios and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust computational super-resolution for nanoscale characterization and quantum-photonic device fabrication.