ArticleNature methods2021
Evaluation and development of deep neural networks for image super-resolution in optical microscopy.
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 140 papers, 1 of them a synthesis that pooled it.
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Who cites it
140 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
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- Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.Light, science & applications · 2026Article
- Physics-guided self-supervised learning for high-fidelity reconstruction in live-cell imaging.Npj imaging · 2026Article
- Combined TIRF and 3D Super-Resolution Microscopy for Nanoscopic Characterization of Adhesion Molecules on Microvilli.Analytical chemistry · 2026Article
- High-fidelity super-resolution microscopy datasets spanning multispectral to hyperspectral domains via diffractive optics.Scientific data · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
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- Super-Resolution Structured Illumination Microscopy to Study Endocytosis and Cellular Nanoparticle Uptake.ACS nano · 2026Review
- Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields.Nature methods · 2026Article
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- Mechanical force regulates the inhibitory function of PD-1.EMBO reports · 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
- Deep Few-View High-Resolution Photon-Counting CT at Halved Dose for Extremity Imaging.IEEE transactions on medical imaging · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- Intravital mesoscale optical imaging: challenges, techniques, and future perspectives.Biophysics reports · 2026Article
80 more citing papers are in PubMed but not listed here.
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Deep neural networks have enabled astonishing transformations from low-resolution (LR) to super-resolved images. However, whether, and under what imaging conditions, such deep-learning models outperform super-resolution (SR) microscopy is poorly explored. Here, using multimodality structured illumination microscopy (SIM), we first provide an extensive dataset of LR-SR image pairs and evaluate the deep-learning SR models in terms of structural complexity, signal-to-noise ratio and upscaling factor. Second, we devise the deep Fourier channel attention network (DFCAN), which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information about diverse biological structures. Third, we show that DFCAN's Fourier domain focalization enables robust reconstruction of SIM images under low signal-to-noise ratio conditions. We demonstrate that DFCAN achieves comparable image quality to SIM over a tenfold longer duration in multicolor live-cell imaging experiments, which reveal the detailed structures of mitochondrial cristae and nucleoids and the interaction dynamics of organelles and cytoskeleton.
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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.