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

• DOI: 10.1364/oe.26.031430
• Publication Date: 2018-11-14T20:08:06Z
• Authors: Jingjing Wu; Siwei Li; Huiqun Cao; Danying Lin; Bin Yu; Junle Qu
• Keywords: Pinhole (optics)

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