Sparsity based super-resolution optical imaging using correlation information

Traditionally, spatial resolution in optical imaging is limited by diffraction. Although sub-wavelength information is absent in the measurements, state-of-the-art fluorescence based localization techniques such as PALM and STORM manage to achieve spatial resolution of tens of nano-meters, but with limited temporal resolution. A more recent technique super-resolution optical fluctuation imaging (SOFI) exploits the temporal statistical behavior of uncorrelated fluorescence emissions to practically improve the spatial resolution by a factor of two over the diffraction limit, but with considerably faster image capturing. Here we propose to exploit the sparse nature of the fluorophores distribution, combined with a statistical prior of uncorrelated emissions such as in SOFI to achieve spatial resolution comparable to PALM/STORM, while retaining the temporal resolution of SOFI. We demonstrate our method on simulations and show improved results over STORM and SOFI. Our method may facilitate super-resolution imaging and capturing of intra-cellular dynamics within living cells.