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.
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