Particle Restoration: A Novel Image Processing Framework for Improving Real Cryo-EM Image Quality in Single Particle Analysis

Cryo-electron microscopy single particle analysis (cryo-EM SPA) is the most powerful technique for biomacromolecule structure determination. However, many factors such as complicated noise and radiation damage make the quality of cryo-EM images extremely poor, where high-frequency structure details...

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Bibliographic Details
Published in:IEEE Transactions on Computational Biology and Bioinformatics Vol. PP; pp. 1 - 13
Main Authors: Hu, Bin, Zhang, Dong-Xu, Liu, Shi-Qi, Xie, Xiao-Liang, Zhou, Xiao-Hu, Li, Hong-Jia, Zheng, Qing-Bing, Zhang, Fa, Hou, Zeng-Guang, Xia, Ning-Shao
Format: Journal Article
Language:English
Published: United States IEEE 23.09.2025
Subjects:
Artificial intelligence
Artificial neural networks
Cryo-electron microscopy (cryo-EM)
Deep learning
Degradation
Feature extraction
Image reconstruction
Image restoration
neural networks
particle restoration
Photomicrography
single particle analysis (SPA)
Three-dimensional displays
Training
ISSN:2998-4165, 2998-4165
Online Access:Get full text
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Summary:Cryo-electron microscopy single particle analysis (cryo-EM SPA) is the most powerful technique for biomacromolecule structure determination. However, many factors such as complicated noise and radiation damage make the quality of cryo-EM images extremely poor, where high-frequency structure details are submerged, limiting the application of deep learning and suppressing the resolution of reconstruction. Thus, image restoration is of vital importance. Some related works explore micrograph restoration, but the particles in restored micrographs are still of poor quality. Moreover, the training approach of existing methods uses noisy observations or simulated data as supervision, leading to reduced performance on real cryo-EM data. In this paper, we define the task of particle restoration and propose a novel 4-step framework to this end. Labels are created for each particle image and paired data is collected within our framework, compensating for the absence of ground truth. A deep neural network with encoder-decoder architecture is designed to learn the mapping from degraded particles to high-quality ones, while other networks can also be employed as a plug-and-play module. Three datasets are constructed from real cryo-EM data and extensive experiments are carried out. Both quantitative metrics and qualitative visualization indicate that our framework is effective for cryo-EM particle restoration. It becomes easier to extract particle features after restoration, aiding in SPA and the effective application of deep learning on cryo-EM images. The downstream task experiments of cryo-EM SPA are also conducted, showing that the proposed framework has the potential to improve cryo-EM SPA performance.
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ISSN:2998-4165
2998-4165
DOI:10.1109/TCBBIO.2025.3608557