ArticleNature communications2024
Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- SIMworks-an integrated software suite for quality-controlled augmented quantitative structured illumination microscopy.Nature protocols · 2026Review
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Self-supervised image denoising and restoration method for atomic force microscopy.Nature communications · 2026Article
- Structured illumination microscopy for high SNR 3D imaging from millimeter-thick tissues to cellular dynamics.Innovation (Cambridge (Mass.)) · 2026Article
- Bioinspired Super-Resolution Electrochemiluminescence Imaging Based on Triboelectric Nanogenerator for Electrode-Electrolyte Interface Analysis.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- A physics-informed alternative to Richardson-Lucy deconvolution across SNR regimes without iteration cutoffs.Nature communications · 2026Article
- Physics-guided self-supervised learning for high-fidelity reconstruction in live-cell imaging.Npj imaging · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- Article
- Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.Science advances · 2026Article
- Self-Supervised Isotropic Resolution Enhancement of Expansion Microscopy via Quantized Compression.Journal of imaging informatics in medicine · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Article
- Intravital mesoscale optical imaging: challenges, techniques, and future perspectives.Biophysics reports · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- FPM2Stain Net: physics-guided super-resolution and multi-modal virtual staining for digital histopathology.Biomedical optics express · 2026Article
- A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.Nature biotechnology · 2026Article
- Real-time, high-throughput super-resolution microscopy via panoramic integration.Nature communications · 2025Article
- NeuroDecon: A Neural Network-Based Method for Three-Dimensional Deconvolution of Fluorescent Microscopic Images.International journal of molecular sciences · 2025Article
- Compressive Fourier-Domain Intensity Coupling (C-FOCUS) enables near-millimeter deep imaging in the intact mouse brain in vivo.Research square · 2025Article
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16 authors.
Funding
Abstract
Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.
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