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Reconstruction of structured illumination microscopy with an untrained neural network
Liu, Xin1; Li, Jinze2; Fang, Xiang1; Li, Jiaoyue1; Zheng, Juanjuan1,3; Li, Jianlang1; Ali, Nauman1; Zuo, Chao1,4; Gao, Peng1; An, Sha1
Department瞬态光学研究室
2023-06-15
Source PublicationOPTICS COMMUNICATIONS
ISSN0030-4018;1873-0310
Volume537
Contribution Rank3
Abstract

Structured illumination microscopy (SIM) is one of super-resolution optical microscopic techniques, and it has been widely used in biological research. In this paper, a physics-driven deep image prior framework for super-resolution reconstruction of SIM (entitled DIP-SIM) is proposed. DIP-SIM does not rely on a large number of labeled data, and the output becomes more interpretable due to the intrinsic constraint of a physical model. Both the simulation and experiment verify that DIP-SIM can reconstruct a super-resolution image with a quality comparable to conventional SIM. Of note, it allows for super-resolution reconstruction from three raw images for two-orientation SIM and four raw images for three-orientation SIM, and hence it has a much faster imaging speed and lower photobleaching compared with the traditional SIM. We can envisage that the proposed method can be applied to chemistry and biomedical fields, etc.

KeywordStructured illumination microscopy Deep learning Neural network Super-resolution Image reconstruction
DOI10.1016/j.optcom.2023.129431
Indexed BySCI
Language英语
WOS IDWOS:001162906900001
PublisherELSEVIER
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/97228
Collection瞬态光学研究室
Corresponding AuthorZuo, Chao; Gao, Peng; An, Sha
Affiliation1.Xidian Univ, Sch Phys, Xian, Peoples R China
2.Xidian Univ, Sch Optoelect Engn, Xian 710071, Peoples R China
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
4.Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Smart Computat Imaging Lab SCILab, Nanjing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Xin,Li, Jinze,Fang, Xiang,et al. Reconstruction of structured illumination microscopy with an untrained neural network[J]. OPTICS COMMUNICATIONS,2023,537.
APA Liu, Xin.,Li, Jinze.,Fang, Xiang.,Li, Jiaoyue.,Zheng, Juanjuan.,...&An, Sha.(2023).Reconstruction of structured illumination microscopy with an untrained neural network.OPTICS COMMUNICATIONS,537.
MLA Liu, Xin,et al."Reconstruction of structured illumination microscopy with an untrained neural network".OPTICS COMMUNICATIONS 537(2023).
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