Bayesian Super-resolution Drift Inference

Single-molecule localization based super-resolution microscopy requires accurate sample drift correction in order to achieve good results. BaSDI implements a Bayesian statistical algorithm that estimate amount of the sample drift for every image frame from the raw dataset. The inference requires no fiducial marker but requires the assumption that the drift is mostly smooth over time. A detailed description of the statistical framework for this algorithm is published (see reference below).



Download link for most recent version: BaSDI (Requires Matlab)

For tracking the development of the software you can check its code repository at GitHub.


Elmokadem A, Yu J, Optimal Drift Correction for Super-resolution Localization Microscopy with Bayesian Inference, Biophys J, in press(2015)


Super-resolution Imaging and Single Molecule Tracking Software

The Octane is a program we developed to facilitate works involved in super-resolution optical imaging (PALM, STORM, etc.). By providing an intuitive graphical user interface front end, we hope it can serve as a useful tool for a wide range of scientists, including experimental biologists as well as physicists. The program runs as a plugin of the (extremely versatile) ImageJ software, thus can be used on any image format that is supported by ImageJ.

Octane screen shot showing options    Octane screen shot showing trajectories


The current version is 1.5.1. To install, download the program and simply copy it under the plugin folder of your ImageJ installation. NOTE: This version requires a newer version of the math library.

Download Octane

Source code repository is online at

What's new in this version – Release Notes.


Older Versions