当前位置: X-MOL 学术Opt. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Fast analysis method for stochastic optical reconstruction microscopy using multiple measurement vector model sparse Bayesian learning
Optics Letters ( IF 3.6 ) Pub Date : 2018-08-10 , DOI: 10.1364/ol.43.003977
Jingjing Wu , Siwei Li , Saiwen Zhang , Danying Lin , Bin Yu , Junle Qu

Compressed sensing (CS) can be used in fluorescence microscopy to improve the temporal resolution of stochastic optical reconstruction microscopy (STORM). Currently, most algorithms used in CS-STORM belong to the single measurement vector (SMV) model, where each super-resolution image is recovered individually from a raw frame, thereby prolonging the computational time. Here, we apply the multiple measurement vector (MMV) model CS algorithm to STORM, wherein all raw images are converted into a matrix and recovered by solving the simultaneous sparse recovery problem. We use the MMV model-based sparse Bayesian learning (SBL) algorithm to reconstitute the raw images of STORM, then compare its imaging resolution and run time with the SMV model CS algorithms. The simulated and experimentally recovered super-resolution images prove that the resolution of MMV model SBL (M-SBL) is comparable with the SMV model algorithm, while the run time is far less and decreases from several hours to several minutes. The high resolution and shorter reconstitution time make M-SBL a promising real-time image reconstruction method for CS-STORM.

中文翻译:

基于多测量矢量模型稀疏贝叶斯学习的随机光学重建显微镜快速分析方法

压缩传感(CS)可用于荧光显微镜,以提高随机光学重建显微镜(STORM)的时间分辨率。当前,CS-STORM中使用的大多数算法属于单个测量向量(SMV)模型,其中每个超分辨率图像都是从原始帧中单独恢复的,从而延长了计算时间。在这里,我们将多测量向量(MMV)模型CS算法应用于STORM,其中所有原始图像都转换为矩阵,并通过解决同时稀疏恢复问题进行恢复。我们使用基于MMV模型的稀疏贝叶斯学习(SBL)算法来重构STORM的原始图像,然后将其成像分辨率和运行时间与SMV模型CS算法进行比较。仿真和实验恢复的超分辨率图像证明,MMV模型SBL(M-SBL)的分辨率可与SMV模型算法相媲美,而运行时间却要短得多,并且从几小时减少到几分钟。高分辨率和更短的重建时间使M-SBL成为CS-STORM的有前途的实时图像重建方法。
更新日期:2018-08-15
down
wechat
bug