Structured illumination microscopy based on principal component analysis

Abstract Structured illumination microscopy (SIM) is one of the powerful super-resolution modalities in bioscience with advantages full-field imaging and high photon efficiency. However, artifact-free image reconstruction requires precise knowledge about parameters. The sample- environment-dependent on-the-fly experimental parameters need to be retrieved a posteriori from acquired data, posing major challenge for real-time, long-term live-cell imaging, where low photobleaching, phototoxicity, light dose are must. In this work, we present an efficient robust SIM algorithm based on principal component analysis (PCA-SIM). PCA-SIM observation that ideal phasor matrix pattern rank one, leading complexity, identification noninteger pixel wave vector phase while rejecting components unrelated parameter estimation. We demonstrate achieves non-iteratively fast, accurate (below 0.01-pixel 0.1 $$\%$$ % 2 $$\pi$$ π relative under typical noise level), estimation at SNRs, which allows real-time live cells complicated scenarios other state-of-the-art methods inevitably fail. particular, provide open-source MATLAB toolbox our associated datasets. combination iteration-free reconstruction, robustness noise, limited computational complexity makes promising method high-speed, long-term, cells.