Filtering enhanced tomographic PIV reconstruction based on deep neural networks
Filtering enhanced tomographic PIV reconstruction based on deep neural networks
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DOI：
10.1049/iet-csr.2019.0040
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Jiaming Liang;Shengze Cai;Chao Xu;Jian Chu
中科院分区：
文献类型：
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作者：
Jiaming Liang;Shengze Cai;Chao Xu;Jian Chu
近年来，层析粒子图像测速技术（Tomo-PIV）在三维流场测量中得到了成功的应用。这种技术高度依赖于通过使用来自不同视角的多个相机的图像来提供空间颗粒分布的重建技术。乘法代数重建技术（MART）作为最常用的重建方法，具有计算速度快、重建精度高等优点。然而，在要重建的密集颗粒分布的情况下，精度不令人满意。为了克服这个问题，本文提出了一种对称的编码-解码全卷积网络，以提高重建质量的MART。神经网络的输入是由MART方法重建的粒子场，而输出是具有相同分辨率的再生图像。数值计算表明，这些模糊或不规则的粒子可以显着细化训练的神经网络。大多数幽灵粒子也可以通过这种过滤方法去除。在不增加计算量的情况下，重建精度可提高10%以上。实验结果表明，训练后的神经网络也可以提供类似的满意的重建和改进的速度场。
Tomographic particle image velocimetry (Tomo-PIV) has been successfully applied in measuring three-dimensional (3D) flow field in recent years. Such technology highly relies on the reconstruction technique which provides the spatial particle distribution by using images from multiple cameras at different viewing angles. As the most popular reconstruction method, the multiplicative algebraic reconstruction technique (MART) has advantages in high computational speed and high accuracy for low particle seeding reconstruction. However, the accuracy is not satisfactory in the case of dense particle distributions to be reconstructed. To overcome this problem, a symmetric encode–decoder fully convolutional network is proposed in this paper to improve the reconstruction quality of MART. The input of the neural network is the particle field reconstructed by the MART approach, while the output is the regenerated image with the same resolution. Numerical evaluations indicate that those blurred or irregular particles can be significantly refined by the trained neural network. Most of the ghost particles can also be removed by this filtering method. The reconstruction accuracy can be improved by more than 10% without increasing the computational cost. Experimental evaluations indicate that the trained neural network can also provide similar satisfactory reconstruction and improved velocity fields.