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

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

Light Sci Appl. 2023 Feb 8;12(1):41. doi: 10.1038/s41377-022-01043-9.

Authors

Xin Chen^{1

2}, Yiwei Hou^{1

2}, Peng Xi^{3

4}

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.
³ Department of Biomedical Engineering, College of Future Technology, Peking University, 100871, Beijing, China. xipeng@pku.edu.cn.
⁴ National Biomedical Imaging Center, Peking University, 100871, Beijing, China. xipeng@pku.edu.cn.

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.