ArticleIUCrJ2020
Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks.
Article in IUCrJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- Distinct associations of pioneer factor Ascl1-E12a with nucleosomes drive changes in cell fate.Molecular cell · 2026Article
- CryoPromptSeg: prompt-guided segmentation with integrated denoising for cryo-EM particle picking.Bioinformatics (Oxford, England) · 2026Article
- A comprehensive foundation model for cryo-EM image processing.Nature methods · 2026Article
- CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.Scientific reports · 2025Article
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
- Cryo-EM structure of AAA + ATPase thorase reveals novel helical filament formation.Computational and structural biotechnology journal · 2025Article
- Article
- Overcoming resolution attenuation during tilted cryo-EM data collection.Nature communications · 2024Article
- Joint micrograph denoising and protein localization in cryo-electron microscopy.Biological imaging · 2024Article
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
- Scaling up cryo-EM for biology and chemistry: The journey from niche technology to mainstream method.Structure (London, England : 1993) · 2023Review
- Signal enhancement for two-dimensional cryo-EM data processing.Biological imaging · 2023Article
- Multiple-image super-resolution of cryo-electron micrographs based on deep internal learning.Biological imaging · 2023Article
- Ab-initio contrast estimation and denoising of cryo-EM images.Computer methods and programs in biomedicine · 2022Article
- Low-dose shift- and rotation-invariant diffraction recognition imaging.Scientific reports · 2022Article
- Cryo-electron tomography related radiation-damage parameters for individual-molecule 3D structure determination.Frontiers in chemistry · 2022Review
- Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry.Scientific reports · 2021Article
- Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules.Frontiers in bioinformatics · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.
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