Super-Resolution Microscopy with Dense Grid After Interpolation

Abstract

The conventional camera image’s pixel size of super-resolution (SR) microscopy is almost the point spread function’s standard deviation, and the grid of a SR image is 1/8 of the pixel size in conventional compressed sensing-based SR microscopy. Here, based on smaller grid size and smaller pixel size, we proposed and generated different measurement matrices, and then compared and analyzed the SR reconstruction results based on the interpolated conventional camera image and different measurement matrices. The quality of the measurement matrix is related to the interpolation’s size. The larger the interpolation’s size, the better its performance. The quality of SR reconstruction depends not only on the measurement matrix’s performance, but also on the grid size. It is found that dense grid based on the size of interpolation equal to 2 can help to obtain the best SR reconstruction in simulation experiments when added Gaussian noise is lower.