Open AccessFiltering enhanced tomographic PIV reconstruction based on deep neural networks
Author(s) -
Liang Jiaming,
Cai Shengze,
Xu Chao,
Chu Jian
Publication year - 2020
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/iet-csr.2019.0040
Subject(s) - artificial intelligence , algebraic reconstruction technique , computer vision , computer science , iterative reconstruction , convolutional neural network , artificial neural network , particle image velocimetry , tomographic reconstruction , field (mathematics) , algorithm , mathematics , physics , pure mathematics , turbulence , thermodynamics
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.