Improved Imaging Performance in Super-Resolution Localization Microscopy by YALL1 Method

In super-resolution localization microscopy, e.g., stochastic optical reconstruction microscopy or photoactivated localization microscopy, a long acquisition time is required because of stochastic imaging nature, which limits its application in dynamic imaging for live cell. To overcome the limitation, one approach based on compressed sensing (CS) has been used in the previous reports. However, the imaging performance obtained by this method may be affected due to the use of interior point method (IPM). To address the problem, in this paper, we introduce an alternative CS reconstruction method and apply the recently developed YALL1 (your algorithm for L1 norm problems) method to super-resolution imaging model. Two types of numerical simulation experiments were performed to evaluate the performance of the proposed method. In case 1, the microscopy data from a single frame was simulated, which was used to evaluate the performance of YALL1 in single-emitter detection. In case 2, the dynamic microscopy data from a series of time points was generated, which was used to evaluate the performance of YALL1 in resolving fine structures. The results show that compared with the previous reported IPM method, the localization accuracy of super-resolution is improved by the proposed YALL1 method, even if there is high emitter density and noise in measurement data. In addition, the imaging time can also be reduced, because fewer imaging cycles are required for reconstructing the final super-resolution image by YALL1 method. Hence, the technique provides the potential in imaging fast cellular processes.