Flow Matching for Fluctuation-Based Fluorescence Super-Resolution
Abstract
Fluctuation-based super-resolution techniques are an appealing alternative to single-molecule and structured-illumination microscopy, as they adapt to standard acquisition settings and exploit the independent stochastic fluctuations of fluorophores across a sequence of frames. However, most existing approaches lack an explicit model of the acquisition process and provide only relative intensity estimates, limiting quantitative analyses. In this work, we combine model-based and generative approaches to exploit the full stochastic information in the sequence, proposing a reconstruction framework built on flow matching and a double-Poisson forward model accounting for blinking, blur, under-sampling and noise. The method alternates two steps: a flow matching model first learns the distribution of the acquired data; then, the sample and the background are jointly estimated by minimising a Kullback-Leibler divergence between observed and simulated distributions via regularised proximal gradient steps. On simulated and real Argolight data, the method resolves lines separated by 120 nm, outperforming SACD and eSRRF.
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BibTeX
@misc{cozzani2026flow,
title = {{Flow Matching for Fluctuation-Based Fluorescence Super-Resolution}},
author = {Alberto Cozzani and Giacomo Meanti and Laure Blanc-Féraud and Luca Calatroni},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37802},
eprint = {2610.37802},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37802},
}
RIS
TY - UNPB
TI - Flow Matching for Fluctuation-Based Fluorescence Super-Resolution
AU - Alberto Cozzani
AU - Giacomo Meanti
AU - Laure Blanc-Féraud
AU - Luca Calatroni
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37802
UR - https://arxiv.org/abs/2610.37802
AB - Fluctuation-based super-resolution techniques are an appealing alternative to single-molecule and structured-illumination microscopy, as they adapt to standard acquisition settings and exploit the independent stochastic fluctuations of fluorophores across a sequence of frames. However, most existing approaches lack an explicit model of the acquisition process and provide only relative intensity estimates, limiting quantitative analyses. In this work, we combine model-based and generative approaches to exploit the full stochastic information in the sequence, proposing a reconstruction framework built on flow matching and a double-Poisson forward model accounting for blinking, blur, under-sampling and noise. The method alternates two steps: a flow matching model first learns the distribution of the acquired data; then, the sample and the background are jointly estimated by minimising a Kullback-Leibler divergence between observed and simulated distributions via regularised proximal gradient steps. On simulated and real Argolight data, the method resolves lines separated by 120 nm, outperforming SACD and eSRRF.
ER -