Compressed sensing structured illumination microscopy

Abstract

We propose a new framework for super-resolution structured illumination microscopy (SR-SIM) based on compressed sensing (CS). Our framework addresses several key problems in SIM, including long readout time and photobleaching. CS has the potential to eliminate these problems because it allows the reduction of the number of measurements, can record an image faster, and excites fluorochromes with less excitation light. Key contribution of our proposed method is that sampling and down-modulation of an object scene are simultaneously performed. The impact of our contribution is demonstrated by simulation-based experiments involving computer-generated super-resolution microscopy images, considering reductions in both data quality and quantity.