title: High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion.
abstract: Localization-based super-resolution microscopy (or called localization microscopy) rely on repeated imaging and localization of active molecules, and the spatial resolution enhancement of localization microscopy is built upon the sacrifice of its temporal resolution. Developing algorithms for high-density localization of active molecules is a promising approach to increase the speed of localization microscopy. Here we present a new algorithm called SSM_BIC for such purpose. The SSM_BIC combines the advantages of the Structured Sparse Model (SSM) and the Bayesian Information Criterion (BIC). Through simulation and experimental studies, we evaluate systematically the performance between the SSM_BIC and the conventional Sparse algorithm in high-density localization of active molecules. We show that the SSM_BIC is superior in processing single molecule images with weak signal embedded in strong background.
authors.affiliation: Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Tingwei
authors.initials: T
authors.lastname: Quan
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhu
authors.forename: Xiaomao
authors.initials: X
authors.lastname: Liu
authors.forename: Yongfeng
authors.initials: Y
authors.lastname: Liu
authors.forename: Jiuping
authors.initials: J
authors.lastname: Ding
authors.forename: Shaoqun
authors.initials: S
authors.lastname: Zeng
authors.forename: Zhen-Li
authors.initials: ZL
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D057809:HEK293 Cells; D006801:Humans; D008856:Microscopy, Fluorescence / Q000706:statistics & numerical data*; D055620:Optical Phenomena; D059629:Signal-To-Noise Ratio