Single-frame super-resolution via Sparse Point Optimization
Abstract
Fluorescence microscopy is essential in biological and medical research, providing critical insights into cellular structures. However, limited by optical diffraction and background noise, a substantial amount of hidden information is still unexploited. To address these challenges, we introduce a novel computational method, termed Sparse Point Optimization Theory (SPOT), which accurately localizes fluorescent emitters by solving an optimization problem. Our results demonstrate that SPOT successfully resolves 30 nm fluorescent line pairs, reveals structural details beyond the diffraction limit in both Airyscan and structured illumination microscopy, and outperforms established algorithms in single-molecule localization tasks. This generic method effectively pushes the resolution limit in the presence of noise, and holds great promise for advancing fluorescence microscopy and analysis in cell biology.
Cite
@article{arxiv.2509.08730,
title = {Single-frame super-resolution via Sparse Point Optimization},
author = {Xiaofeng Zhang and Yongsheng Huang and Jielong Yang and Zhili Wang and Si Chen and Linbo Liu and Xin Ge},
journal= {arXiv preprint arXiv:2509.08730},
year = {2026}
}