Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer

doi:10.1093/bioinformatics/btac052

	Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer
	Li, Hongjia 1,2; Zhang, Hui 2,3; Wan, Xiaohua 1; Yang, Zhidong 1,2; Li, Chengmin 3; Li, Jintao 1; Han, Renmin 4; Zhu, Ping 2,3; Zhang, Fa 1
	2022-02-04
发表期刊	BIOINFORMATICS
ISSN	1367-4803
页码	8
摘要	Motivation: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryoEM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. Results: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.
DOI	10.1093/bioinformatics/btac052
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0504700] ; National Key Research and Development Program of China[2020YFA0712401] ; National Key Research and Development Program of China[2021YFF0704300] ; National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[62072280] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[31730023] ; National Natural Science Foundation of China[31521002] ; Chinese Academy of Sciences (CAS)[XDB37010100] ; National Laboratory of Biomacromolecules of China[2019KF07]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000756962500001
出版者	OXFORD UNIV PRESS
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18993
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Han, Renmin; Zhu, Ping; Zhang, Fa
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Biomacromol, Inst Biophys, Natl Lab Biomacromol, Beijing 100101, Peoples R China 4.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Qingdao 266237, Peoples R China
推荐引用方式 GB/T 7714	Li, Hongjia,Zhang, Hui,Wan, Xiaohua,et al. Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer[J]. BIOINFORMATICS,2022:8.
APA	Li, Hongjia.,Zhang, Hui.,Wan, Xiaohua.,Yang, Zhidong.,Li, Chengmin.,...&Zhang, Fa.(2022).Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.BIOINFORMATICS,8.
MLA	Li, Hongjia,et al."Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer".BIOINFORMATICS (2022):8.