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

文献类型：期刊论文

作者Yang, Zhidong2,3,4; Zang, Dawei4; Li, Hongjia2,4; Zhang, Zhao1; Zhang, Fa3; Han, Renmin1
刊名ULTRAMICROSCOPY
出版日期2024
卷号255页码:11
关键词Electron tomography Image denoising Self-supervised learning
ISSN号0304-3991
DOI10.1016/j.ultramic.2023.113860
英文摘要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.
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
语种英语
WOS记录号WOS:001098616600001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/38091]  
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.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, 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.

入库方式： OAI收割

来源：计算技术研究所

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