论文标题
通过贝叶斯非参数模型从Schlieren图像中重建密度重建
Density reconstruction from schlieren images through Bayesian nonparametric models
论文作者
论文摘要
这项研究提出了一种从Schlieren图像中提取定量信息的根本替代方法。该方法使用缩放的，衍生的增强的高斯工艺模型，从水平和垂直方向上的刀边缘从两个相应的schlieren图像中获得真实的密度估计。我们说明了从风洞刺激模型，飞行中的超音速飞机以及高阶数值冲击管模拟拍摄的Schlieren图像的方法。
This study proposes a radically alternate approach for extracting quantitative information from schlieren images. The method uses a scaled, derivative enhanced Gaussian process model to obtain true density estimates from two corresponding schlieren images with the knife-edge at horizontal and vertical orientations. We illustrate our approach on schlieren images taken from a wind tunnel sting model, a supersonic aircraft in flight, and a high-order numerical shock tube simulation.
