Open AccessResolution improvement of multifocal structured illumination microscopy with sparse Bayesian learning algorithm
Author(s) -
Jingjing Wu,
Siwei Li,
Huiqun Cao,
Danying Lin,
Bin Yu,
Junle Qu
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.031430
Subject(s) - deconvolution , computer science , resolution (logic) , optics , microscopy , artificial intelligence , algorithm , computer vision , pinhole (optics) , iterative reconstruction , image resolution , physics
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 (MSIM MSBL ) 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 MSIM MSBL 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.