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

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.