ArticleNature communications2022
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Unsupervised deep learning enables blur-free resolution enhancement in two-photon microscopy.Cell reports methods · 2026Article
- Article
- vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Near-isotropic super-resolution microscopy with axial interference speckle illumination.Nature communications · 2025Article
- Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging.Nature methods · 2025Article
- Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution.Analytical chemistry · 2025Review
- System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring.Nature communications · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Deep Learning Assisted Plasmonic Dark-Field Microscopy for Super-Resolution Label-Free Imaging.Nano letters · 2024Article
- Three-dimensional random-access confocal microscopy with 3D remote focusing system.Communications engineering · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.Nature communications · 2024Article
- Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning.Scientific reports · 2024Article
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
- High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy.Communications biology · 2024Article
- Article
- Super-resolution biomedical imaging via reference-free statistical implicit neural representation.Physics in medicine and biology · 2023Article
- A deep learning-based stripe self-correction method for stitched microscopic images.Nature communications · 2023Article
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.
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