Deep learning enables contrast-robust super-resolution reconstruction in structured illumination microscopy

Abstract

Structured illumination microscopy (SIM) is a powerful technique for super-resolution (SR) image reconstruction. However, conventional SIM methods require high-contrast illumination patterns, which necessitate precision optics and highly stable light sources. To overcome these challenges, we propose a new method called contrast-robust structured illumination microscopy (CR-SIM). CR-SIM employs a deep residual neural network to enhance the quality of SIM imaging, particularly in scenarios involving low-contrast illumination stripes. The key contribution of this study is the achievement of reliable SR image reconstruction even in suboptimal illumination contrast conditions. The results of our study will benefit various scientific disciplines.