Wavelet-Based Optical Flow Analysis for Background-Oriented Schlieren Image Processing

Abstract

A wavelet-based optical flow analysis (wOFA) method for processing background-oriented schlieren (BOS) images is presented and demonstrated on synthetic and experimental data. Optical flow is inherently well suited to BOS, because the background pattern and lighting conditions are specified and controlled by the user and can be chosen to play to the strengths of optical flow processing. Analysis of the synthetic BOS data show that a two-dimensional sinusoidal background produces the highest reconstruction accuracy for both wOFA and iterative least squares (ILS) algorithms. wOFA outperforms ILS in terms of overall accuracy for displacement fields with sufficiently high spatial frequency content. In addition, wOFA provides higher spatial resolution, about an order of magnitude in terms of the total number of pixels in the final BOS image. Finally, wOFA is demonstrated on two sets of experimental data: a heat gun plume experiment with nearly ideal imaging characteristics, and experiments in a supersonic wind tunnel flow with more realistic restrictions on the acquisition of images. BOS images computed with wOFA are shown to have higher spatial resolution and sensitivity than ILS, without introducing additional noise. Therefore, wOFA of BOS images are able to reveal flow features not detected by ILS analysis.