ℓ1-regularized maximum likelihood estimation with focused-spot illumination quadruples the diffraction-limited resolution in fluorescence microscopy.

Abstract

Super-resolution fluorescence microscopy has proven to be a useful tool in biological studies. To achieve more than two-fold resolution improvement over the diffraction limit, existing methods require exploitation of the physical properties of the fluorophores. Recently, it has been demonstrated that achieving more than two-fold resolution improvement without such exploitation is possible using only a focused illumination spot and numerical post-processing. However, how the achievable resolution is affected by the processing step has not been thoroughly investigated. In this paper, we focus on the processing aspect of this emerging super-resolution microscopy technique. Based on a careful examination of the dominant noise source and the available prior information in the image, we find that if a processing scheme is appropriate for the dominant noise model in the image and can utilize the prior information in the form of sparsity, improved accuracy can be expected. Based on simulation results, we identify an improved processing scheme and apply it in a real-world experiment to super-resolve a known calibration sample. We show an improved super-resolution of 60nm, approximately four times beyond the conventional diffraction-limited resolution.