Denoising of scanning electron microscope images for biological ultrastructure enhancement

doi:10.1142/S021972002250007X

CORC > 自动化研究所 > 中国科学院自动化研究所 > 类脑智能研究中心 > 微观重建与智能分析

	Denoising of scanning electron microscope images for biological ultrastructure enhancement
	Chang, Sheng 4,5; Shen, Lijun 5; Li, Linlin 5; Chen, Xi 5; Han, Hua 1,2,3,5
刊名	JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
	2022-06-01
卷号	20 期号:03 页码:21
关键词	SEM noise model denoising variance stabilization transformation two-stage multi-loss deep learning
ISSN号	0219-7200
DOI	10.1142/S021972002250007X
通讯作者	Chen, Xi(xi.chen@ia.ac.cn) ; Han, Hua(hua.han@ia.ac.cn)
英文摘要	Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. Source code is available on GitHub (https://github.com/VictorCSheng/VSID-Net).
资助项目	Strategic Priority Research Program of Chinese Academy of Science[XDB32030208] ; Instrument function development innovation program of Chinese Academy of Sciences[E0S92308] ; Bureau of International Cooperation, CAS[153D31KYSB20170059]
WOS关键词	NOISE REMOVAL ; POISSON
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematical & Computational Biology
语种	英语
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
WOS记录号	WOS:000829660700006
资助机构	Strategic Priority Research Program of Chinese Academy of Science ; Instrument function development innovation program of Chinese Academy of Sciences ; Bureau of International Cooperation, CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49749]
专题	类脑智能研究中心_微观重建与智能分析
通讯作者	Chen, Xi; Han, Hua
作者单位	1.CASIA, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 3.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chang, Sheng,Shen, Lijun,Li, Linlin,et al. Denoising of scanning electron microscope images for biological ultrastructure enhancement[J]. JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY,2022,20(03):21.
APA	Chang, Sheng,Shen, Lijun,Li, Linlin,Chen, Xi,&Han, Hua.(2022).Denoising of scanning electron microscope images for biological ultrastructure enhancement.JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY,20(03),21.
MLA	Chang, Sheng,et al."Denoising of scanning electron microscope images for biological ultrastructure enhancement".JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 20.03(2022):21.