Peer-reviewedCC BYACS nanoscience Au
Universal Computational Super-Resolution Framework for Imaging of Quantum Dots.
9 affiliations
- 1Department of Optics, Faculty of Science, Palacký University, 17. listopadu 12, 77900 Olomouc, Czechia.
- 2Department of Physics, Stockholm University, 10691 Stockholm, Sweden.
- 3Institut of Physics, University of Oldenburg, D-26129 Oldenburg, Germany.
- 4University of Applied Sciences Emden/Leer, 26723 Emden, Germany.
- 5Technische Physik, Physikalisches Institut and Würzburg-Dresden Cluster of Excellence ct.qmat, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany.
- 6Institute of Applied Physics, Abbe Center of Photonics, Friedrich Schiller University Jena, 07743 Jena, Germany.
- 7Fraunhofer-Institute for Applied Optics and Precision Engineering IOF, 07743 Jena, Germany.
- 8Max-Planck-School of Photonics, 07743 Jena, Germany.
- 9Materials Science and Engineering, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, Arizona 85287, United States.
Abstract
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.
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BibTeX
@article{vasinka2026universal,
title = {{Universal Computational Super-Resolution Framework for Imaging of Quantum Dots.}},
author = {Dominik Vašinka and Jaewon Lee and Charlie Stalker and Victor Mitryakhin and Ivan Solovev and Sven Stephan and Sven Höfling and Falk Eilenberger and Seth Ariel Tongay and Christian Schneider and Miroslav Ježek and Ana Predojević},
journal = {ACS nanoscience Au},
year = {2026},
month = aug,
doi = {10.1021/acsnanoscienceau.5c00198},
url = {https://europepmc.org/article/MED/42921104},
note = {CC BY},
}
RIS
TY - JOUR
TI - Universal Computational Super-Resolution Framework for Imaging of Quantum Dots.
AU - Dominik Vašinka
AU - Jaewon Lee
AU - Charlie Stalker
AU - Victor Mitryakhin
AU - Ivan Solovev
AU - Sven Stephan
AU - Sven Höfling
AU - Falk Eilenberger
AU - Seth Ariel Tongay
AU - Christian Schneider
AU - Miroslav Ježek
AU - Ana Predojević
PY - 2026
DA - 2026/08/05
JO - ACS nanoscience Au
DO - 10.1021/acsnanoscienceau.5c00198
UR - https://europepmc.org/article/MED/42921104
AB - 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.
SN - 2694-2496
ER -