sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy

Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM their superior sensitivity, resolution, field of view, frame rates. However, the unique single-pixel-dependent readout noise may lead reconstruction artefacts affect accuracy subsequent statistical analysis. We first established a nonuniform model address this issue, which incorporates offset, gain, variance based on process. The simulation indicates that pixel-dependent causes reconstructed superresolution (SR) image. Thus, we propose novel noise-corrected algorithm derived from model, can effectively suppress noise-related improve signal-to-noise ratio (SNR).