Deep learning approach for nonlinear structured illumination microscopy

Abstract

We propose that Deep Learning (DL) can be used to improve the performance of nonlinear structured illumination microscopy(NSIM) to enable it to reconstruct a super-resolution image with much less raw image frames. This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the NSIM imaging process. We validate our approach by super-resolution image reconstruction of simulated obtained data.