Quantitative analysis of the flow field is an effective method to study hydrodynamics. As a flow field measurement technology, the Background Oriented Schlieren (BOS) is widely used. However, it is difficult to measure the complex transparent flow field (flow field with large refractive index gradient) using the BOS experiment. In order to overcome this difficulty and improve the accuracy of the BOS experiment, this paper presents a hybrid adaptive wavelet-based optical flow algorithm for the BOS. The current algorithm is a combination of the traditional optical flow algorithm and the wavelet-based optical flow algorithm. By adding the initial value constraints, the adaptive scale constraints, and the adaptive regularization constraints, the algorithm can effectively overcome the above-mentioned difficulty and also improve its accuracy. To further illustrate the feasibility of the proposed method, this paper uses the simulation data, the data of the DNS datasets, and the data of the BOS experiment to verify the performance of the algorithm. The experiment of comparing the proposed algorithm with the existing ones is carried out on the DNS datasets and the data of the BOS experiment. Finally, the proposed method is verified by a practical BOS experiment. The results show that the proposed algorithm can effectively improve the measurement accuracy of displacements.

中文翻译：

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面向背景的Schlieren（BOS）实验的基于混合小波的自适应光流算法

流场的定量分析是研究流体力学的有效方法。背景技术作为背景的流场测量技术，已广泛使用。但是，使用BOS实验很难测量复杂的透明流场（折射率梯度大的流场）。为了克服这一困难并提高BOS实验的准确性，本文提出了一种基于混合小波的自适应自适应BOS光流算法。当前算法是传统光流算法和基于小波的光流算法的结合。通过添加初始值约束，自适应尺度约束和自适应正则约束，该算法可以有效克服上述困难，提高精度。为了进一步说明该方法的可行性，本文利用仿真数据，DNS数据集数据和BOS实验数据验证了算法的性能。在DNS数据集和BOS实验数据上进行了将所提算法与现有算法进行比较的实验。最后，通过实际的BOS实验验证了该方法的有效性。结果表明，该算法可以有效提高位移的测量精度。在DNS数据集和BOS实验数据上进行了将所提算法与现有算法进行比较的实验。最后，通过实际的BOS实验验证了该方法的有效性。结果表明，该算法可以有效提高位移的测量精度。在DNS数据集和BOS实验数据上进行了将所提算法与现有算法进行比较的实验。最后，通过实际的BOS实验验证了该方法的有效性。结果表明，该算法可以有效提高位移的测量精度。