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dc.contributor.authorZhang, Heng-
dc.contributor.authorZhu, Yunqi-
dc.contributor.authorJin, Luhong-
dc.contributor.authorYang, Haixu-
dc.contributor.authorWang, Jianhang-
dc.contributor.authorAblameyko, Sergey-
dc.contributor.authorLiu, Xu-
dc.contributor.authorXu, Yingke-
dc.date.accessioned2025-12-23T08:59:47Z-
dc.date.available2025-12-23T08:59:47Z-
dc.date.issued2025-
dc.identifier.citationSmall Methods. 2025 Mar 3;9(5).ru
dc.identifier.urihttps://elib.bsu.by/handle/123456789/339329-
dc.description.abstractStructured illumination microscopy (SIM) has emerged as a pivotalsuper-resolution technique in biological imaging. This review aims tointroduce the fundamental principles of SIM, primarily focuses on the latestdevelopments in super-resolution SIM imaging, such as the light illuminationand modulation devices, and the image reconstruction algorithms.Additionally, the application of deep learning (DL) technology in SIM imagingis explored, which is employed to enhance image quality, accelerate imagingand reconstruction speed or replace the current image reconstructionmethod. Furthermore, the key evaluation metrics are proposed and discussedfor assessment of deep-learning neural networks, especially for theiremployment in SIM. Finally, the future integration of artificial intelligence (AI)with SIM system and the perspective of smart microscope are also discussedru
dc.description.sponsorshipH.Z. and Y.Z. contributed equally to this work. This work was sup-ported by the National Key Research and Development Program of China(2021YFF0700305), Zhejiang Provincial Natural Science Foundation (Nos.LZ23H180002 and 2024C03056), Zhejiang University K.P.Chao’s HighTechnology Development Foundation (2022RC009), and the Fundamen-tal Research Funds for the Central Universities (226-2024-00059).ru
dc.language.isoenru
dc.publisherWileyru
dc.rightsinfo:eu-repo/semantics/openAccessru
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Механикаru
dc.titleRecent Advances in Structured Illumination Microscopy: From Fundamental Principles to AI-Enhanced Imagingru
dc.typearticleru
dc.rights.licenseCC BY 4.0ru
dc.identifier.DOI10.1002/smtd.202401616-
dc.identifier.scopus85218968438-
Располагается в коллекциях:Кафедра веб-технологий и компьютерного моделирования (статьи)

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