ArticleNature methods2021
Deep learning-based point-scanning super-resolution imaging.
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 63 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed.
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Optically programmable dual-band perovskite single-pixel detector for color image encryption.Light, science & applications · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SmartEM: machine learning-guided electron microscopy.Nature methods · 2026Article
- Technological advances in visualizing and rewiring microtubules during plant development.Journal of experimental botany · 2025Review
- Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM.Nature communications · 2025Article
- Deep learning reveals how cells pull, buckle, and navigate fibrous environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Comprehensive Review of Optical Metrology and Perception Technologies.Sensors (Basel, Switzerland) · 2025Review
- Cellular optical imaging techniques: a dynamic advancing frontier.Science China. Life sciences · 2025Review
- Descattering and image restoration with a transformer-based neural network in deep tissue imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Correcting Non-Uniform Milling in FIB-SEM Images with Unsupervised Cross-Plane Image-to-Image Translation.bioRxiv : the preprint server for biology · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images.Communications biology · 2025Article
- Non-Uniformity Correction of Spatial Object Images Using Multi-Scale Residual Cycle Network (CycleMRSNet).Sensors (Basel, Switzerland) · 2025Article
- Optical sectioning methods in three-dimensional bioimaging.Light, science & applications · 2025Review
- PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy.BMC methods · 2025Article
- Self-inspired learning for denoising live-cell super-resolution microscopy.Nature methods · 2024Article
- Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy.Nature communications · 2024Article
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of deep learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a 'crappifier' that computationally degrades high SNR, high-pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence time-lapse data, we developed a 'multi-frame' PSSR approach that uses information in adjacent frames to improve model predictions. PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed and sensitivity. All the training data, models and code for PSSR are publicly available at 3DEM.org.
Indexed as
Identifiers
What OpenQuestion holds
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.