ArticleNature methods2021
Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed, 207 citations in OpenAlex.
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- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution.Nature communications · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured illumination microscopy.Nature methods · 2026Article
- CHARMS: A CNN-Transformer Hybrid with Attention Regularization for MRI Super-Resolution.Sensors (Basel, Switzerland) · 2026Article
- Enhancing volumetric microscopy with blind computational correction of spatially variant aberrations using neural field representation.bioRxiv : the preprint server for biology · 2026Article
- Diffusion prior-based zero-shot denoising for real-world fluorescence microscopy images via encoder-decoder guided sampling.Biomedical optics express · 2026Article
- A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.Nature biotechnology · 2026Article
- Leveraging spatial-angular redundancy for self-supervised denoising of 3D fluorescence imaging without temporal dependency.Nature communications · 2025Article
- Cellular optical imaging techniques: a dynamic advancing frontier.Science China. Life sciences · 2025Review
- Near-isotropic super-resolution microscopy with axial interference speckle illumination.Nature communications · 2025Article
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- NeuroDecon: A Neural Network-Based Method for Three-Dimensional Deconvolution of Fluorescent Microscopic Images.International journal of molecular sciences · 2025Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
18 authors at 4 institutions in 2 countries.
Funding
Abstract
We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.