Velocity estimation for sequences of sparse images

粒子图像测速技术(PIV)和粒子跟踪测速技术(PTV)是表征流动特性的重要成像技术。它们被应用于许多不同的领域，从燃烧过程的优化到船舶推进系统。注入到液体流动中的颗粒必须用高速的高空间分辨率的ccd或cmos摄像机进行检测和跟踪。同时，可以检测和跟踪数千个粒子的三维运动，空间分辨率为0.1像素，从而能够表征瞬时和湍流。PIV和其他多维成像技术的基本局限性是缺乏对高时间分辨率(100赫兹以上的帧速率)的实时处理。因此，成像技术不能应用于过程测量技术。此外，kHz范围内的帧速率所需的数据速率非常高，因此最先进的RAM的有限大小将过程观察时间限制在只有几秒，这不足以进行有意义的分析。在第一个项目阶段，已经证明了通过相关和空间滤波技术直接估计运动矢量场和通过压缩感知重建稀疏PTV信号的适用性。一方面，项目扩展将侧重于有条不紊的新方法。一种创新的方法是利用改进的光学概念直接估计运动矢量场。此外，在稀疏信号的重构中，应充分利用先验知识，而相位恢复方法将克服传感器的非线性问题。另一方面，通过模拟获得的结果应得到实验验证。因此，为了实现滤光片，必须对现有的实验系统进行扩展。对所研究方法的适用性的证明将是朝着更有效和更强大的PIV/PTV技术迈出的重要一步。

Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) are important imaging techniques for flow characterization. They are applied in many different areas from the optimization of combustion processes to propulsion systems of ships. Particles injected into the liquid flow have to be detected and tracked by using high speed CCD or CMOS cameras with high spatial resolution. Meanwhile, three-dimensional motions of several thousand particles can be detected and tracked with a spatial resolution of 0.1 pixels allowing the characterization of instationary and turbulent flows. The basic limitation of PIV and other multi-dimensional imaging techniques is the lack of real-time processing for a high temporal resolution (frame rates above 100Hz). Hence, imaging techniques cannot be applied for process measurement technology. Furthermore, the required data rates for frame rates in the kHz range are extremely high so that the limited size of state-of-the-art RAMs restricts the process observation time to only a few seconds which is not sufficient for a meaningful analysis. In the first project phase, the suitability of the direct estimation of a motion vector field by correlation and spatial filtering techniques and the reconstruction of sparse PTV signals by compressed sensing has been shown. On the one hand, the project extension will focus on methodically new methods. An innovative approach directly estimates the motion vector field by using modified optical concept. Furthermore, priors shall be intensively used to improve the reconstruction of sparse signals, and phase retrieval approaches shall overcome the nonlinear sensor problem. On the other hand, results obtained by simulations shall be experimentally verified. Therefore, an existing experimental system has to be extended by components for implementing an optical filter. The proof of suitability for the examined approaches will be a major step towards a more efficient and powerful PIV / PTV technique.