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Denoising of scanning electron microscope images for biological ultrastructure enhancement | |
Chang, Sheng1,2; Shen, Lijun1; Li, Linlin1; Chen, Xi1; Han, Hua1,3,4,5 | |
发表期刊 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
ISSN | 0219-7200 |
2022-06-01 | |
卷号 | 20期号:03页码:21 |
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). |
关键词 | SEM noise model denoising variance stabilization transformation two-stage multi-loss deep learning |
DOI | 10.1142/S021972002250007X |
关键词[WOS] | NOISE REMOVAL ; POISSON |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | Strategic Priority Research Program of Chinese Academy of Science ; Instrument function development innovation program of Chinese Academy of Sciences ; Bureau of International Cooperation, CAS |
WOS研究方向 | Biochemistry & Molecular Biology ; Computer Science ; Mathematical & Computational Biology |
WOS类目 | Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology |
WOS记录号 | WOS:000829660700006 |
出版者 | WORLD SCIENTIFIC PUBL CO PTE LTD |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49750 |
专题 | 脑图谱与类脑智能实验室_微观重建与智能分析 |
通讯作者 | Chen, Xi; Han, Hua |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 5.CASIA, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
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. |
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