Reconstruction of super-resolution STORM images using compressed sensing based on low-resolution raw images and interpolation

Abstract

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

Super-Resolution Image Reconstruction Based on Single-Molecule Localization Algorithm

“…Among these algorithms, MLE, Gauss fitting and gradient fitting are considered more accurate, while centroid and fluoroBancroft methods are much faster in the localization of molecules [12,15]. Until now, scientists are still making unremitting efforts to improve the performance of localization algorithms in super-resolution imaging technology [17][18][19][20].…”

Super-Resolution Imaging with Graphene

“…This intrinsic limit, also known as diffraction limit [ 29 , 30 ], has become the main obstacle to high-resolution optical imaging. Thus far, a great number of novel methods for achieving super-resolution imaging have been proposed and demonstrated experimentally in both near field and far field, such as near-field scanning optical microscopy (NSOM) [ 31 , 32 , 33 ]; far-field superlens (FSL) [ 34 , 35 , 36 ], hyperlens [ 37 , 38 , 39 ], and metalens [ 40 , 41 , 42 , 43 ]; stimulated emission depletion microscopy (STED) [ 44 , 45 , 46 , 47 ]; stochastic optical reconstruction microscopy (STORM) [ 48 , 49 , 50 , 51 , 52 ]; structured illumination microscopy (SIM) [ 53 , 54 , 55 ]; plasmonic structured illumination microscopy derived from SIM [ 56 , 57 , 58 ], and so on [ 59 , 60 ]. It is worth mentioning that graphene-related materials exhibit different properties according to their lateral size, number of layers and oxidation degree.…”

Wide spectrum denoising (WSD) for super-resolution microscopy imaging using compressed sensing and a high-resolution camera

“…CS can be used to acquire and reconstruct raw images of high-density fluorescent molecules and greatly improve the temporal and spatial resolutions of super-resolution microscopy [4,[10][11][12][13][14]. Cheng et al [15] noted that if a high-resolution camera is used to acquire data, its corresponding measurement matrix has better performance.which is helpful in improving the reconstruction effect. However, if a high-resolution camera is used, the raw image's noise increases and the reconstruction effect simultaneously deteriorates.…”

“…Therefore, based on CS and high-resolution cameras, the quality of a reconstructed super-resolution image depends on the balance between the performance improvement of the measurement matrix and the increase in the raw image's noise. If the effect of the measurement matrix performance improvement is greater than the adverse effect of the increased noise, the reconstruction effect will increase; otherwise, the opposite will occur [15]. If the noise of the raw image can be effectively denoised, the temporal and spatial resolutions of the CS-based super-resolution microscopy can be further improved.…”

“…[16,22,23]. However, various studies on fluorescent molecular localization in the field of super-resolution microscopy rarely report denoising of the raw image before localization [4,15,24]. Single-molecule localization algorithms such as (fluorescence) photoactivated localization microscopy ((f)PALM) perform bandpass filter processing on the raw image before localization [5].…”