Abstract:Single-molecule-localization-based super-resolution microscopic technologies, such as stochastic optical reconstruction microscopy (STORM), require lengthy runtimes. Compressed sensing (CS) can partially overcome this inherent disadvantage, but its effect on super-resolution reconstruction has not been thoroughly examined. In CS, measurement matrices play more important roles than reconstruction algorithms. Larger measurement matrices have better restricted isometry properties (RIPs). This paper proposes, analyzes, and compares uses of higher resolution cameras and interpolation to achieve better outcomes. Statistical results demonstrate that super-resolution reconstructions is significantly improved by interpolating low-resolution STORM raw images and using point-spread-function-based measurement matrices with better RIPs. The analysis of publically accessible experimental data confirms this conclusion. Ye, "3D high-density localization microscopy using hybrid astigmatic/ biplane imaging and sparse image reconstruction," Biomed. Opt. Express 5(11), 3935-3948 (2014). 11. S. J. Holden, S. Uphoff, and A. N. Kapanidis, "DAOSTORM: an algorithm for high-density super-resolution microscopy," Nat. Methods 8(4), 279-280 (2011). 12. T. Quan, H. Zhu, X. Liu, Y. Liu, J. Ding, S. Zeng, and Z.-L. Huang, "High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion," Opt. Express 19(18), 16963-16974 (2011). 13. L. Zhu, W. Zhang, D. Elnatan, and B. Huang, "Faster STORM using compressed sensing," Nat. Methods 9(7), 721-723 (2012). 14. H. P. Babcock, J. R. Moffitt, Y. Cao, and X. Zhuang, "Fast compressed sensing analysis for super-resolution imaging using L1-homotopy," Opt. Express 21(23), 28583-28596 (2013). 15. S. Zhang, D. Chen, and H. Niu, "3D localization of high particle density images using sparse recovery," Appl.Opt. 54(26), 7859-7864 (2015). 16. M. Ovesný, P. Křížek, Z. Švindrych, and G. M. Hagen, "High density 3D localization microscopy using sparse support recovery," Opt. Express 22(25), 31263-31276 (2014