Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM

Xin Chen; Yiwei Hou; Peng Xi

doi:10.1038/s41377-022-01043-9

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Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM

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- Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM
- Light: Science & Applications Vol. 12, Issue 2, Pages: 150-152(2023)
- Affiliations：
  
  1.Department of Biomedical Engineering, College of Future Technology, Peking University, 100871, Beijing, China
  2.National Biomedical Imaging Center, Peking University, 100871, Beijing, China
- Author bio：
  
  Peng Xi (xipeng@pku.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-01043-9
  CLC：
- Published：28 February 2023，
  
  Published Online：8 February 2023，
Scan for full text
Chen, X., Hou, Y. W. & Xi, P. Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM. Light: Science & Applications, 12, 150-152 (2023).
DOI：

Chen, X., Hou, Y. W. & Xi, P. Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM. Light: Science & Applications, 12, 150-152 (2023). DOI： 10.1038/s41377-022-01043-9.

摘要

Abstract

Principal component analysis (PCA)

a common dimensionality reduction method

is introduced into SIM to identify the frequency vectors and pattern phases of the illumination pattern with precise subpixel accuracy

fast speed

and noise-robustness

which is promising for real-time and long-term live-cell imaging.

关键词

Keywords

references

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