Parameter estimation algorithm based on the combination of cross-correlation and principal component analysis for structured illumination microscopy

Structured illumination microscopy (SIM) is more applicable to the super-resolution imaging of living cells by virtue of its wide field of view, fast imaging and low phototoxicity. However, a high-quality super-resolution image requires accurate parameter estimation. Recently, we have proposed an efficient and robust SIM algorithm based on principal component analysis (PCA-SIM) that integrates iteration-free reconstruction, noise robustness, and limited computational complexity. Nevertheless, as with many parameter estimation algorithms, the performance of PCA-SIM may be affected when using high-frequency sinusoidal illumination and total internal reflection fluorescence (TIRF) objective. In this work, we present a parameter estimation method of combining cross-correlation and principal component analysis, capable of accurate sub-pixel precision estimation when the 1-order spectral information is lacking without iteration, promising to achieve high-speed, long-term, artifact-free super-resolution imaging of live cells.