Li Jia, Chen Danni, Qu Junle
Opt Express. 2018 Sep 17;26(19):24329-24342. doi: 10.1364/OE.26.024329.
High-density molecules localization algorithm is crucial to obtain sufficient temporal resolution in super-resolution fluorescence microscopy, particularly in view of the challenges associated with live-cell imaging. In this work, an algorithm based on augmented Lagrangian method (ALM) is proposed for reconstructing high-density molecules. The problem is firstly converted to an equivalent optimization problem with constraints using variable splitting, and then the alternating minimization method is applied to implement it straightforwardly. We also take advantage of quasi-Newton method to tackle the sub-problems for acceleration, and total variation regularization to reduce noise. Numerical results on both simulated and real data demonstrate that the algorithm can achieve using fewer frames of raw images to reconstruct high-resolution image with favorable performance in terms of detection rate and image quality.
高密度分子定位算法对于在超分辨率荧光显微镜中获得足够的时间分辨率至关重要，特别是考虑到与活细胞成像相关的挑战。在这项工作中，提出了一种基于增广拉格朗日方法（ALM）的算法来重建高密度分子。该问题首先通过变量分裂转换为一个带约束的等效优化问题，然后应用交替最小化方法直接实现。我们还利用拟牛顿法来处理子问题以加速，并采用总变差正则化来降低噪声。在模拟数据和真实数据上的数值结果表明，该算法可以使用更少的原始图像帧数来重建高分辨率图像，在检测率和图像质量方面具有良好的性能。
