An image processing and optimal nonlinear filtering technique for particle image velocimetry of microflows

We have developed a processing methodology for extracting accurate measurements from ensembles of image pairs of particle-laden steady microflows. Processing the subimages and their correlations produces an estimate of the particle-displacement probability-density function that improves with ensemble averaging. An optimal nonlinear filter precisely extracts flow measurements from the measured displacement distribution. This methodology has been used to dissect the pressure-driven component from the electrokinetic component of particle motion in particle-image videos with no depth resolution of a planar microflow past an obstacle array and bubble.