ArticleNature methods2025
Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- High numerical aperture confocal volumetric mesoscope reveals mesoscale subcellular dynamics in vivo.Nature biotechnology · 2026Article
- Physics-Enhanced Deep Learning Optimized Semitransparent Organic Photovoltaics for Building-Integrated Sustainable Energy.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Real-time robust autofocus method enabling sustained intravital scanning light field imaging.Nature communications · 2026Article
- Foveated light-field compound imager.Science advances · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- High-fidelity intravital imaging of biological dynamics with latent-space-enhanced digital adaptive optics.Nature biotechnology · 2026Article
- Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.Science advances · 2026Article
- Foveated Light-Field Compound Imager.bioRxiv : the preprint server for biology · 2026Article
- Patch deconvolution for Fourier light-field microscopy.Biophysical journal · 2026Article
- Fluorescent Labeling Methods for Brain Structure Research.Molecules (Basel, Switzerland) · 2026Review
- Neutrophil and macrophage zonation in liver disease: from spatiotemporal dynamics to advanced computational analysis.Frontiers in immunology · 2026Review
- Snapshot 3D at the speed frontier: redefining light-field microscopy for neuroimaging.PhotoniX · 2026Article
- Leveraging spatial-angular redundancy for self-supervised denoising of 3D fluorescence imaging without temporal dependency.Nature communications · 2025Article
- Capturing more than meets the eye.Nature methods · 2025Article
- Review
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Authors and funding
10 authors.
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Abstract
Light-field microscopy (LFM) and its variants have significantly advanced intravital high-speed 3D imaging. However, their practical applications remain limited due to trade-offs among processing speed, fidelity, and generalization in existing reconstruction methods. Here we propose a physics-driven self-supervised reconstruction network (SeReNet) for unscanned LFM and scanning LFM (sLFM) to achieve near-diffraction-limited resolution at millisecond-level processing speed. SeReNet leverages 4D information priors to not only achieve better generalization than existing deep-learning methods, especially under challenging conditions such as strong noise, optical aberration, and sample motion, but also improve processing speed by 700 times over iterative tomography. Axial performance can be further enhanced via fine-tuning as an optional add-on with compromised generalization. We demonstrate these advantages by imaging living cells, zebrafish embryos and larvae, Caenorhabditis elegans, and mice. Equipped with SeReNet, sLFM now enables continuous day-long high-speed 3D subcellular imaging with over 300,000 volumes of large-scale intercellular dynamics, such as immune responses and neural activities, leading to widespread practical biological applications.
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