STORM原始图像和基于点扩散函数测量矩阵的压缩感知后处理方法  

Compressive Sensing Post Processing of STORM's Raw Images and Measurement Matrix Based on PSF

作　　者：程涛[1,2,3] 陈丹妮[1,3] 于斌[1,3] 

机构地区：[1]深圳大学光电工程学院光电子器件与系统(教育部/广东省)重点实验室,广东深圳518060 [2]广西科技大学汽车与交通学院,广西柳州545006 [3]深圳大学光电工程学院深圳生物医学光学微纳检测与成像重点实验室,广东深圳518060

出　　处：《探测与控制学报》2017年第4期31-38,共8页Journal of Detection & Control

基　　金：国家重点基础研究发展计划项目资助(2012CB825802);国家自然科学基金资助项目(61335001;61178080;61235012;11004136;41461082;81660296);国家重大科学仪器设备开发专项项目资助(2012YQ15009203);中国博士后科学基金项目资助(2016M592525);广西自然科学基金项目资助(2014GXNSFAA118285)

摘　　要：针对随机光学重构显微等超分辨率显微成像技术存在的图像采集重构慢、空间分辨率低、时间分辨率弱,基于点扩散函数测量矩阵的约束等距性差和重构效果不好等缺点,提出了随机光学重构显微(STORM)原始图像和基于点扩散函数测量矩阵的压缩感知后处理方法。仿真结果表明,该方法在不改变显微镜光学系统的前提下,通过对STORM原始图像和基于点扩散函数测量矩阵的后处理,能够大幅提高超分辨率显微成像的重构效果。Stochastic optical reconstruction microscopy（STORM）and other super resolution microscopy imaging technology have shortcomings such as slow acquisition and reconstruction of images,low spatial and time resolution,bad restricted isometry property（RIP）and bad reconstruction effect.The compressed sensing post processing method of STORM raw images and measurement matrices based on Point Spread Function（PSF）was proposed.This method enabled the reconstruction effect of super-resolution microscopy imaging to be significantly improved and enhanced by postprocessing of STORM raw images and measurement matrices based on PSF under the premise of not changing microscope optical system.A new way was provided for improve the resolution of microscopic images.The simulation results showed that the accurate reconstruction probability of measurement matrices based on PSF of three different compression ratios was greatly improved after the processing.SNR was improved by 14.13 dB,121.97 dB and 140.08 dB in a set of experiments respectively.

关 键 词：压缩感知 随机光学重构显微 点扩散函数 超分辨显微成像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]