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Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured

Jiahao Liu1, Xue Dong2, Huaide Lu3

  • 1Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.

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|December 13, 2025
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Summary
This summary is machine-generated.

Self-supervised reconstruction (SSR)-SIM advances live-cell super-resolution microscopy by eliminating the need for ground-truth data. This deep-learning approach enhances reconstruction precision for studying dynamic biostructures.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • Deep-learning (DL) based structured illumination microscopy (SIM) shows promise for live-cell super-resolution imaging.
  • Training DL models typically requires ground-truth (GT) data, which is challenging to acquire for SIM.
  • Existing methods without GT compromise image fidelity and resolution.

Purpose of the Study:

  • To develop a self-supervised reconstruction method for SIM that removes the need for GT data.
  • To improve reconstruction precision and fidelity in DL-based SIM.
  • To enable robust long-term super-resolution imaging of dynamic biological processes.

Main Methods:

  • Developed self-supervised reconstruction (SSR)-SIM by integrating statistical analysis of reconstruction artifacts.
  • Incorporated structured light modulation priors into the DL network.
  • Validated SSR-SIM on diverse biological datasets without GT training data.

Main Results:

  • SSR-SIM successfully reconstructed super-resolution images without requiring GT data.
  • The method demonstrated improved reconstruction precision compared to existing approaches.
  • Enabled long-term imaging of dynamic cellular events, including cytoskeletal remodeling, mitochondrial dynamics, viral interactions, and intercellular transfer.

Conclusions:

  • SSR-SIM offers a robust and versatile approach for super-resolution live-cell imaging.
  • The method overcomes the GT data acquisition bottleneck in DL-SIM.
  • Facilitates advanced studies of subcellular dynamics and interactions in live biological systems.