Fast two-dimensional super-resolution image reconstruction algorithm for ultra-high emitter density

作     者：Huang, Jiaqing Gumpper, Kristyn Chi, Yuejie Sun, Mingzhai Ma, Jianjie 

作者机构：Ohio State Univ Davis Heart & Lung Res Inst Dept Surg Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA 

出 版 物：《OPTICS LETTERS》 (光学快报)

年 卷 期：2015年第40卷第13期

页      面：2989-2992页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：NIH [AG028614, AR061385, HL069000] Ralph E. Power Junior Faculty Enhancement Award from the Oak Ridge Associated Universities 

主　　题：Image processing Superresolution Multiframe image processing Microscopy 

摘      要：Single-molecule localization microscopy achieves sub-diffraction-limit resolution by localizing a sparse subset of stochastically activated emitters in each frame. Its temporal resolution is limited by the maximal emitter density that can be handled by the image reconstruction algorithms. Multiple algorithms have been developed to accurately locate the emitters even when they have significant overlaps. Currently, compressive-sensing-based algorithm (CSSTORM) achieves the highest emitter density. However, CSSTORM is extremely computationally expensive, which limits its practical application. Here, we develop a new algorithm (MempSTORM) based on two-dimensional spectrum analysis. With the same localization accuracy and recall rate, MempSTORM is 100 times faster than CSSTORM with l(1)-homotopy. In addition, MempSTORM can be implemented on a GPU for parallelism, which can further increase its computational speed and make it possible for online super-resolution reconstruction of high-density emitters. (C) 2015 Optical Society of America