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A set of programs to aid in the acquisition and image analysis of data in “photoactivated localization microscopy” (PALM) and “stochastic optical reconstruction microscopy” (STORM). QuickPALM provides a complete solution for acquisition, reconstruction and visualization of 3D PALM or STORM images, achieving resolutions of ~40 nm in real time. This software package should greatly facilitate the conversion of many laser-excitation widefield or TIRF microscopes into powerful super-resolution microscopes.


Provides a user-friendly means of visualizing, filtering and analyzing localization microscopy (LM) data. PALMsiever includes drift correction, clustering, intelligent line profiles, many rendering algorithms, and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software.


An interactive open-source software with a graphical user interface, which allows performing processing steps for localization data in an integrated manner. This includes common features and new tools such as correction of chromatic aberrations, drift correction based on iterative cross-correlation calculations, selection of localization events, reconstruction of 2D and 3D datasets in different representations, estimation of resolution by Fourier ring correlation, clustering analysis based on Voronoi diagrams and Ripley’s functions. SharpViSu is optimized to work with eventlist tables exported from most popular localization software. The functionality of SharpViSu is extendable via plugins, such as ClusterViSu for comprehensive cluster analysis of localization microscopy data. It includes tools such as calculations of Voronoi and Ripley statistics with Monte-Carlo simulations, different modes of reconstruction (e.g. based on Gaussian blur or Ripley’s functions) and segmentation of density maps, retrieval of geometrical properties of detected clusters, segmentation based on Voronoi tessellation.

FOCAL / Fast Optimized Cluster Algorithm for Localizations

A grid-based clustering algorithm FOCAL, which explicitly accounts for several dominant artifacts arising in SMLM image reconstructions. FOCAL is fast and efficient, scaling like O(n), and only has one set parameter. We assess DBSCAN and FOCAL on experimental dSTORM data of clusters of eukaryotic RNAP II and PALM data of the bacterial protein H-NS, then provide a detailed comparison via simulation. FOCAL performs comparable and often superior to DBSCAN while yielding a significantly faster analysis. Additionally, FOCAL provides a novel method for filtering out of focus clusters from complex SMLM images.

GraspJ / GPU-Run Analysis for STORM and PALM

An open source, real-time data analysis and rendering tool for super-resolution imaging techniques that are based on single molecule detection and localization (e.g. stochastic optical reconstruction microscopy - STORM and photoactivation localization microscopy – PALM). GraspJ is an ImageJ plug-in with a convenient user interface, that allows high accuracy localization of single molecules as well as processing and rendering of high resolution images in real-time. GraspJ includes several features such as drift correction, multi-color, 3D analysis/rendering, and is compatible with a large range of data acquisition software. In addition, it allows easy interfacing with other image processing tools available with ImageJ.


A software package based on Bayesian statistics and requires no user dependent parameters for molecule detection and image reconstruction for single-molecule localization microscopy (SMLM), including photoactivated localization microscope (PALM), stochastic optical reconstruction microscope (STORM), and direct stochastic optical reconstruction microscopy (dSTORM), etc. Auto-Bayes is useful for the researchers using SMLM without being experts in single molecule spectroscopy and super-resolution fluorescence microscopy.