Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM
Xin Chen, Yiwei Hou, Peng Xi
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.
Topics & Concepts
Principal component analysisSubpixel renderingPattern recognition (psychology)Dimensionality reductionArtificial intelligenceRobustness (evolution)Curse of dimensionalitySparse PCAComputer scienceMathematicsPixelBiochemistryGeneChemistryAdvanced Fluorescence Microscopy TechniquesCell Image Analysis TechniquesImage Processing Techniques and Applications