High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion
High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion
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DOI：
10.1364/oe.19.016963
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发表时间：
2011-08-29
期刊：
影响因子：
3.8
通讯作者：
Huang, Zhen-Li
中科院分区：
文献类型：
--
作者：
Quan, Tingwei;Zhu, Hongyu;Huang, Zhen-Li
基于定位的超分辨率显微术（或称为定位显微术）依赖于对活性分子的重复成像和定位，而定位显微术的空间分辨率增强是建立在牺牲其时间分辨率的基础上的。开发高密度定位活性分子的算法是提高定位显微镜速度的一种有前途的方法。在这里，我们提出了一个新的算法称为SSM_BIC。SSM_BIC结合了结构化稀疏模型（SSM）和贝叶斯信息准则（BIC）的优点。通过仿真和实验研究，系统地评价了SSM_BIC算法和传统Sparse算法在高密度活性分子定位中的性能。我们表明，SSM_BIC是上级在处理单分子图像与弱信号嵌入在强背景。(C)2011年美国光学学会
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. (C) 2011 Optical Society of America