Localization based super-resolution microscopy techniques require precise drift correction methods because the achieved spatial resolution is close to both the mechanical and optical performance limits of modern light microscopes. Multi-color imaging methods require corrections in addition to those dealing with drift due to the static, but spatially-dependent, chromatic offset between images. We present computer simulations to quantify this effect, which is primarily caused by the high-NA objectives used in super-resolution microscopy. Although the chromatic offset in well corrected systems is only a fraction of an optical wavelength in magnitude (<50 nm) and thus negligible in traditional diffraction limited imaging, we show that object colocalization by multi-color super-resolution methods is impossible without appropriate image correction. The simulated data are in excellent agreement with experiments using fluorescent beads excited and localized at multiple wavelengths. Finally we present a rigorous and practical calibration protocol to correct for chromatic optical offset, and demonstrate its efficacy for the imaging of transferrin receptor protein colocalization in HeLa cells using two-color direct stochastic optical reconstruction microscopy (dSTORM).

Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.

Optimization of sample, imaging and data processing parameters is an essential task in localization based super-resolution microscopy, where the final image quality strongly depends on the imaging of single isolated fluorescent molecules. A computational solution that uses a simulator software for the generation of test data stacks was proposed, developed and tested. The implemented advanced physical models such as scalar and vector based point spread functions, polarization sensitive detection, drift, spectral crosstalk, structured background etc., made the simulation results more realistic and helped us interpret the final super-resolved images and distinguish between real structures and imaging artefacts.

The reduction of out of focus signal is a general task in fluorescence microscopy and is especially important in the recently developed super-resolution techniques because of the degradation of the final image. Several illumination methods have been developed to provide decreased out of focus signal level relative to the common epifluorescent illumination. In this paper we examine the highly inclined and the total internal reflection illumination techniques using the ray tracing method. Two merit functions were introduced for the quantitative description of the excitation of the selected region. We studied the feasibility of illumination methods, and the required corrections arising from the imperfections of the optical elements.

Interpretation of high resolution images provided by localization-based microscopy techniques is a challenge due to imaging artefacts that can be categorized by their origin. They can be introduced by the optical system, by the studied sample or by the applied algorithms. Some artefacts can be eliminated via precise calibration procedures, others can be reduced only below a certain value. Images studied both theoretically and experimentally are qualified either by pattern specific metrics or by a more general metric based on fluorescence correlation spectroscopy.