ArticleBiomedical optics express2019
High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
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Who cites it
36 citing papers in PubMed.
- High-fidelity super-resolution microscopy datasets spanning multispectral to hyperspectral domains via diffractive optics.Scientific data · 2026Article
- Deep learning-driven conversion of scanning superlens microscopy to high depth-of-field SEM-like imaging.Microsystems & nanoengineering · 2025Article
- Cellular optical imaging techniques: a dynamic advancing frontier.Science China. Life sciences · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Deep Learning Assisted Plasmonic Dark-Field Microscopy for Super-Resolution Label-Free Imaging.Nano letters · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution.Sensors (Basel, Switzerland) · 2024Article
- Exceeding the limit for microscopic image translation with a deep learning-based unified framework.PNAS nexus · 2024Article
- Video-rate 3D imaging of living cells using Fourier view-channel-depth light field microscopy.Communications biology · 2023Article
- Rapid 3D isotropic imaging of whole organ with double-ring light-sheet microscopy and self-learning side-lobe elimination.Biomedical optics express · 2023Article
- Facilitating cell segmentation with the projection-enhancement network.Physical biology · 2023Article
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- Tile-based microscopic image processing for malaria screening using a deep learning approach.BMC medical imaging · 2023Article
- Small training dataset convolutional neural networks for application-specific super-resolution microscopy.Journal of biomedical optics · 2023Article
- Deep learning-based high-speed, large-field, and high-resolution multiphoton imaging.Biomedical optics express · 2023Article
- Super-resolution and segmentation deep learning for breast cancer histopathology image analysis.Biomedical optics express · 2023Article
- A super-resolution network using channel attention retention for pathology images.PeerJ. Computer science · 2023Article
- Super-resolution Segmentation Network for Reconstruction of Packed Neurites.Neuroinformatics · 2022Article
- Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.Nature communications · 2022Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We combine a generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstruction of these samples verifies a successful GAN-based single image super-resolution procedure. We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training data set preparation. After a well-trained network has been created, this deep learning-based imaging approach is capable of recovering a large FOV (~95 mm
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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.