Up sampled PSF enables high accuracy 3D super-resolution imaging with sparse sampling rate

Abstract

Single-molecule localization microscopy (SMLM) provides nanoscale imaging, but pixel integration of acquired SMLM images limited the choice of sample rate which restricts the information content conveyed within each image. We propose an up-sampled point spread function (PSF) inverse modeling method for large-pixel single molecule localization, enabling precise 3D super-resolution imaging with sparse sampling rate. Our approach could reduce data volume or expand the field of view by nearly an order of magnitude, while maintaining high localization accuracy, greatly improving the imaging throughput with limited pixels offered by the existing cameras.