Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising

doi:10.1016/j.ultramic.2023.113860

	Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising
	Yang, Zhidong 1,2,3; Zang, Dawei 1; Li, Hongjia 1,3; Zhang, Zhao 4; Zhang, Fa 2; Han, Renmin 4
	2024
发表期刊	ULTRAMICROSCOPY
ISSN	0304-3991
卷号	255 页码:11
摘要	Cryo-Electron Tomography (cryo-ET) is a revolutionary technique for visualizing macromolecular structures in near-native states. However, the physical limitations of imaging instruments lead to cryo-ET volumetric images with very low Signal-to-Noise Ratio (SNR) with complex noise, which has a side effect on the downstream analysis of the characteristics of observed macromolecules. Additionally, existing methods for image denoising are difficult to be well generalized to the complex noise in cryo-ET volumes. In this work, we propose a self-supervised deep learning model for cryo-ET volumetric image denoising based on noise modeling and sparsity guidance (NMSG), achieved by learning the noise distribution in noisy cryo-ET volumes and introducing sparsity guidance to ensure smoothness. Firstly, a Generative Adversarial Network (GAN) is utilized to learn noise distribution in cryo-ET volumes and generate noisy volumes pair from single volume. Then, a new loss function is devised to both ensure the recovery of ultrastructure and local smoothness. Experiments are done on five real cryo-ET datasets and three simulated cryo-ET datasets. The comprehensive experimental results demonstrate that our method can perform reliable denoising by training on single noisy volume, achieving better results than state-of-the-art single volume-based methods and competitive with methods trained on large-scale datasets.
关键词	Electron tomography Image denoising Self-supervised learning
DOI	10.1016/j.ultramic.2023.113860
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFF0704300] ; National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[32241027] ; National Natural Science Foundation of China[62072280] ; National Natural Science Foundation of China[62072441] ; Natural Science Foundation of Shandong Province, China[ZR2023YQ057] ; Youth Innovation Promotion Association CAS ; Young Scientists Fund of the National Natural Science Foundation of China[61902373] ; Foundation of the Chinese Academy of Sciences, China[JCPYJJ 22013] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB24050300] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB44030300]
WOS研究方向	Microscopy
WOS类目	Microscopy
WOS记录号	WOS:001098616600001
出版者	ELSEVIER
引用统计	被引频次：2[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/2XEOYT63/38092
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Fa; Han, Renmin
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China 2.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China
推荐引用方式 GB/T 7714	Yang, Zhidong,Zang, Dawei,Li, Hongjia,et al. Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising[J]. ULTRAMICROSCOPY,2024,255:11.
APA	Yang, Zhidong,Zang, Dawei,Li, Hongjia,Zhang, Zhao,Zhang, Fa,&Han, Renmin.(2024).Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising.ULTRAMICROSCOPY,255,11.
MLA	Yang, Zhidong,et al."Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising".ULTRAMICROSCOPY 255(2024):11.