Open AccessDivide and conquer: real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy
Author(s) -
Luchang Li,
Bo Xin,
Weibing Kuang,
Zhiwei Zhou,
Zhen-Li Huang
Publication year - 2019
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.27.021029
Subject(s) - computer science , common emitter , image resolution , microscopy , image processing , resolution (logic) , divide and conquer algorithms , artificial intelligence , population , optics , computer vision , algorithm , image (mathematics) , physics , optoelectronics , demography , sociology
Multi-emitter localization has great potential for maximizing the imaging speed of super-resolution localization microscopy. However, the slow image analysis speed of reported multi-emitter localization algorithms limits their usage in mostly off-line image processing with small image size. Here we adopt the well-known divide and conquer strategy in computer science and present a fitting-based method called QC-STORM for fast multi-emitter localization. Using simulated and experimental data, we verify that QC-STORM is capable of providing real-time full image processing on raw images with 100 µm × 100 µm field of view and 10 ms exposure time, with comparable spatial resolution as the popular fitting-based ThunderSTORM and the up-to-date non-iterative WindSTORM. This study pushes the development and practical use of super-resolution localization microscopy in high-throughput or high-content imaging of cell-to-cell differences or discovering rare events in a large cell population.