Illumination parameter estimation of structured illumination microscopy based on principal component analysis

Abstract

Structured illumination microscopy (SIM) is a powerful super-resolution method in bioscience, featuring full-field imaging and high photon efficiency. However, artifact-free super-resolution image reconstruction requires precise knowledge about the illumination parameters. In this work, we propose an efficient and robust SIM algorithm based on principal component analysis (PCA-SIM) combines iteration-free reconstruction, noise robustness, and limited computational complexity. These characteristics make PCA-SIM a promising method for high-speed, long-term, artifact-free super-resolution imaging of live cells.