Abstract
Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and artifacts, while deep learning methods typically lack generalizability. We introduce 3Snet-CLID, a computational SR method which integrates a hybrid supervised/self-supervised deep learning network for signal-preserving denoising with direct Richardson–Lucy deconvolution. 3Snet-CLID’s per-pixel denoising strategy suppresses noise while maintaining signal distribution, mitigating artifacts, and enhancing robustness. The method achieves more than 5-fold resolution improvement on conventional microscopes, revealing diverse structures such as the mitochondrial outer membrane, endoplasmic reticulum, and nuclear pores in live and fixed cells under standard labeling. By overcoming key computational SR bottlenecks, 3Snet-CLID offers denoising capability and an accessible platform for high-fidelity nanoscale live-cell imaging.
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Data availability
All essential raw datasets, including files for Supplementary Figs. Raw unprocessed images are available at Figshare. Source data are provided with this paper.
Code availability
The tutorials and the updating version of our 3Snet-CLID software can be found at https://github.com/FudongXue-xpyLab/3Snet-CLID.
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Acknowledgements
This project was supported by the National Natural Science Foundation of China (T2394513 P.X., 92254306 P.X., and 32227802 L.Y.), the National Key R&D Program of China (2024YFC3406600 P.X., 2022YFC3400600 L.Y., and 2022ZD0211900 P.X.), and the Strategic Priority Research Program of Chinese Academy of Sciences (XDB37040301 P.X.). We thank Dr. Yuanyuan Li (Institute of Biophysics, Chinese Academy of Sciences) for the help with the DNA PAINT experiment. We thank Dr. Xiaochen Wang (Southern University of Science and Technology) for the gift of C. elegans strain. We thank Dr. JunjianWang and Dr. Hongwei Yang (Technical Institute of Physics and Chemistry, Chinese Academy of Sciences) for the gift of the polymer dots.
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P.X. and L.Y. conceived and supervised the study. F.X. performed CLID processing, F.X. and Z.X. analyzed data, L.Y. performed WF and SD imaging, W.H. build the WF microscopy, J.R., and C.S. performed dual-color imaging of PMP and actin in fixed HeLa cells. S.L. generated Nup96-sfGFP knock-in U-2 OS cell line, M.W. cultured the C. elegans strain. L.C. participated in project discussion. P.X., and L.Y. wrote the manuscript with contributions from all authors.
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P.X., L.Y., F.X., and W.H. have a pending patent application on the presented framework. The remaining authors declare no competing interests.
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Xue, F., Yuan, L., He, W. et al. High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70791-8
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DOI: https://doi.org/10.1038/s41467-026-70791-8
