{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:29:45Z","timestamp":1763202585556,"version":"3.41.2"},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"the Strategic Priority Research Program of Chinese Academy of Science","award":["No. XDB32030200"],"award-info":[{"award-number":["No. XDB32030200"]}]},{"name":"Instrument function development innovation program of Chinese Academy of Sciences","award":["No.E0S92308"],"award-info":[{"award-number":["No.E0S92308"]}]},{"name":"Bureau of International Cooperation, CAS","award":["No. 153D31KYSB20170059"],"award-info":[{"award-number":["No. 153D31KYSB20170059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p> 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 ). <\/jats:p>","DOI":"10.1142\/s021972002250007x","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T05:22:05Z","timestamp":1650950525000},"source":"Crossref","is-referenced-by-count":5,"title":["Denoising of scanning electron microscope images for biological ultrastructure enhancement"],"prefix":"10.1142","volume":"20","author":[{"given":"Sheng","family":"Chang","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.\u00a0R.\u00a0China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, 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200031, P.\u00a0R.\u00a0China"},{"name":"National Laboratory of Pattern Recognition, CASIA, Beijing 100190, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"S021972002250007XBIB001","doi-asserted-by":"publisher","DOI":"10.1038\/nature22356"},{"key":"S021972002250007XBIB002","doi-asserted-by":"publisher","DOI":"10.1126\/science.aay3134"},{"key":"S021972002250007XBIB003","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18659-3"},{"key":"S021972002250007XBIB005","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720017500159"},{"issue":"1","key":"S021972002250007XBIB007","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1002\/sca.1996.4950180108","volume":"18","author":"Oho E","year":"1996","journal-title":"Scanning: J Scanning 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