A dynamic, local masking technique for PIV and Synthetic Schlieren (2005)

Pattern matching is fundamental to both Particle Image Velocimetry (PIV) and Synthetic Schlieren. The hope is always that an imaged pattern changes from one frame to the next only through translation in conjunction with weak rotation and weak (anisotropic) dilation. However, the observed patterns in typical experiments also change in a manner that has a more major impact on the coherence of the pattern. The most significant of these (at least for PIV) is particles entering or leaving the light sheet, thus fundamentally restructuring the observed pattern. Recently, Sveen and Dalziel [2005] have made progress in mitigating the effect of such fundamental pattern changes in an attempt to allow simultaneous, interleaved PIV and Synthetic Schlieren measurements of a stratified flow. Synthetic Schlieren [Dalziel et al., 2000], which relies on the apparent movement of a background pattern due to refractive index variations in the stratification, is contaminated by the presence of a second coherent pattern imposed by the PIV seed particles within the same field of view. An effective strategy to remove artifacts caused by the PIV particles is therefore essential. (Removing the effect of the Synthetic Schlieren pattern on the PIV measurements is much more straightforward.) The first critical step processing a Synthetic Schlieren image contaminated by PIV particles, is to identify the parts of the image that are contaminated. As described in Sveen and Dalziel [2005], this can be accomplished by using a secondary LCD monitor to display the backgrounds dynamically. This monitor is positioned behind the wave tank and is used to display two patterns: the first is a completely white background which is used for PIV, and the second is a strongly textured background used for Synthetic Schlieren. Particles in the fluid will appear as dark blobs on the white background and their motion and position may be determined by the use of PIV or PTV. The strongly textured background will appear to move due to refractive index variations and this is central to Synthetic Schlieren. Before processing the Synthetic Schlieren image, the image containing the particles is used to define a mask based on the particle positions. By taking the particle motion into account we are able to remove most of the influence of the particles from the Synthetic Schlieren image. This leads to an improvement in precision and quality of the calculated density measurements, as shown in Sveen and Dalziel [2005]. In one respect Synthetic Schlieren is a much simpler problem than PIV: the base pattern being analysed remains fixed; it is only the presence of the particles that cause it to change. In contrast, PIV patterns change in response to particles moving into or out of the light sheet. As Hu et al. [1998] demonstrated, this change in the set of particles imaged can lead to substantial errors in PIV velocity estimates. However, as Sveen and Dalziel [2005] have demonstrated for Synthetic Schlieren, these errors can be reduced substantially by the simple expedient of filtering out any artifacts that do not belong to the coherent pattern being analysed. Motivated by their findings we propose to filter out large image differences on a pixel by pixel basis. In the framework of squared Euclidean differences (also known as Minimum Quadratic Differences, or MQD), where the correlation is defined as R(m; n) = X i X j