{
  "abstract": "Structured illumination microscopy (SIM) enables rapid, long-term super-resolution (SR) imaging of live-cell dynamics. However, current SIM reconstruction methods, although achieving high structural fidelity, generally lack reliable intensity quantification. Here we present qHiFi-SIM, a physics-driven self-supervised learning framework for quantitative high-fidelity SIM imaging. By leveraging the wide-field (WF) image as a physical intensity reference and conventional SIM reconstruction as a structural continuity prior, qHiFi-SIM enables self-supervised training without ground-truth intensity data. It achieves structural fidelity comparable to HiFi-SIM while maintaining excellent intensity linearity at the SIM point-spread function scale (R2 > 0.986, mean intensity ratio near 1). The framework transfers across diverse SIM setups and typical samples, and directly improves the intensity linearity of other state-of-the-art algorithms without retraining. We demonstrate its utility for live-cell quantitative visualization of mitochondrial intensity dynamics and high-fidelity SIM-FRET functional imaging, offering a practical tool for SR structural visualization and quantitative functional imaging in live cells.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Breast Cancer Precision Medicine, Institute of Biomedical Engineering, Kunming Medical University, Kunming 650500, China"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Y. Tang"
    },
    {
      "name": "Z. Luo"
    },
    {
      "name": "X. Zhu"
    },
    {
      "name": "W. Wang"
    },
    {
      "name": "X. Ge"
    },
    {
      "name": "M. Li"
    },
    {
      "name": "C. Chen"
    },
    {
      "name": "T. Chen"
    },
    {
      "name": "C. Chen"
    },
    {
      "name": "P. Xi"
    },
    {
      "affs": [
        0
      ],
      "name": "G. Wen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qghkezhfessm.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.06.12.731778",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.12.731778v2.full",
  "id": "2qghkezhfessm",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.12.731778v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "cell biology"
    ],
    [
      "openalex",
      "1304|Advanced Fluorescence Microscopy Techniques"
    ]
  ],
  "openalex": "W7164907581",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.12.731778v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2qghkezhfessm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:29Z",
      "native_id": "10.64898/2026.06.12.731778",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.12.731778v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:cell biology",
      "subfield": "cell-biology"
    }
  ],
  "title": "Physics-driven self-supervised learning for quantitative high-fidelity structured illumination microscopy",
  "title_text": "Physics-driven self-supervised learning for quantitative high-fidelity structured illumination microscopy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:29Z",
  "url": "https://research.draconitic.com/paper/2qghkezhfessm",
  "venue": "bioRxiv",
  "versions": []
}