基于随机采样的超高分辨率成像中快速压缩感知分析

Fast compressed sensing analysis for super-resolution imaging using random sampling operator

摘要: 随着近年来超分辨成像技术的发展，基于单分子拟合的超分辨成像方法能够实现纳米尺度的空间分辨率，但这种方法的耗时较长，时间分辨率较差。成像重构时间较长主要受制于成像过程中每帧图像较低的荧光分子密度，所以需要足够多的采样帧数来重构一张图像。文中提出一种利用随机采样的快速压缩感知算法，结合分块压缩感知重构算法，最终能够在高分子密度的条件下获得较快的重构速度及较高的定位精度。

Abstract: Single molecule fitting-based super-resolution imaging methods achieve nanoscale image resolution but suffer from a long time resolution. The long acquisition time is limited by the low molecule density of fluorescent molecules that can be localized per imaging frame. In this paper, a fast compressed sensing method was proposed to use random sampling operator, together with the block compressed sensing reconstruction method, have a good performance of localization accuracy under high molecular density and reconstruction speed.