Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM
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-t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2023-02-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-022-01043-9 |
| _version_ | 1811165711287975936 |
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| author | Xin Chen Yiwei Hou Peng Xi |
| author_facet | Xin Chen Yiwei Hou Peng Xi |
| author_sort | Xin Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T15:41:06Z |
| format | Article |
| id | doaj.art-8a81e67b0a0e411e9a153510ff3bcf00 |
| institution | Directory Open Access Journal |
| issn | 2047-7538 |
| language | English |
| last_indexed | 2024-04-10T15:41:06Z |
| publishDate | 2023-02-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj.art-8a81e67b0a0e411e9a153510ff3bcf002023-02-12T12:23:12ZengNature Publishing GroupLight: Science & Applications2047-75382023-02-011211310.1038/s41377-022-01043-9Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIMXin Chen0Yiwei Hou1Peng Xi2Department of Biomedical Engineering, College of Future Technology, Peking UniversityDepartment of Biomedical Engineering, College of Future Technology, Peking UniversityDepartment of Biomedical Engineering, College of Future Technology, Peking UniversityAbstract 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.https://doi.org/10.1038/s41377-022-01043-9 |
| spellingShingle | Xin Chen Yiwei Hou Peng Xi Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM Light: Science & Applications |
| title | Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM |
| title_full | Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM |
| title_fullStr | Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM |
| title_full_unstemmed | Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM |
| title_short | Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM |
| title_sort | parameter estimation of the structured illumination pattern based on principal component analysis pca pca sim |
| url | https://doi.org/10.1038/s41377-022-01043-9 |
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