In the recent past, single-molecule based localization or photoswitching microscopy methods such as stochastic optical reconstruction microscopy (STORM) or photoactivated localization microscopy (PALM) have been successfully implemented for subdiffraction-resolution fluorescence imaging. However, the computational effort needed to localize numerous fluorophores is tremendous, causing long data processing times and thereby limiting the applicability of the technique. Here we present a new computational scheme for data processing consisting of noise reduction, detection of likely fluorophore positions, high-precision fluorophore localization and subsequent visualization of found fluorophore positions in a super-resolution image. We present and benchmark different algorithms for noise reduction and demonstrate the use of non-maximum suppression to quickly find likely fluorophore positions in high depth and very noisy images. The algorithm is evaluated and compared in terms of speed, accuracy and robustness by means of simulated data. On real biological samples, we find that real-time data processing is possible and that super-resolution imaging with organic fluorophores of cellular structures with approximately 20 nm optical resolution can be completed in less than 10 s.