Resolution improvement of multifocal structured illumination microscopy with sparse Bayesian learning algorithm.

Abstract

Multifocal structured illumination microscopy (MSIM) is the parallelized version of image scanning microscopy (ISM), which uses multiple diffraction limited spots, instead of a single diffraction limited spot, to increase the imaging speed. By adding pinhole, contraction and deconvolution, a twofold resolution enhancement could be achieved in theory. However, this resolution improvement is difficult to be attained in practice. In this work, without any modification of the experimental setup, we propose to use multiple measurement vector (MMV) model sparse Bayesian learning (MSBL) algorithm (MSIMMSBL) as the reconstruction algorithm of MSIM, which does not need to estimate the illumination patterns but treat the reconstruct process as an MMV signal reconstruction problem. We compare the reconstructed super-resolution images of MSIMMSBL and MSIM by using simulation and experimental raw images. The results prove that by using the MSBL algorithm, the MSIM can obtain a higher than twofold resolution enhancement compared with the wide field image. This outstanding imaging resolution combining with the primary advantages of MSIM, such as the high imaging speed, could promote the application of MSIM in super-resolution microscopy imaging technology.