Performance of a gradient-based shift estimator in a spatially sparse data environment: tracking the sub-pixel motion...

Through a series of numerical simulations, we investigate the suitability of a relatively new gradient-based particle-tracking algorithm for efficiently quantifying sub-pixel shifts of fluorescently labelled cells or particles from a sequence of video microscopy images. The algorithm excels at estimating sub-0.5 pixel per frame shifts in both data-dense (e.g. laser speckle imaging) and data-sparse (e.g. fluorescence imaging) applications. No upsampling (i.e. interpolation) is required to achieve the sub-pixel shift resolution, and thus the approach avoids the complexity and potential errors associated with the interpolation process. An efficient MATLAB sub-routine is provided for implementing the algorithm.