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Filtering enhanced tomographic PIV reconstruction based on deep neural networks

Content Provider	IET Digital Library
Author	Liang, Jiaming Cai, Shengze Xu, Chao Chu, Jian
Abstract	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.
Starting Page	43
Ending Page	52
Page Count	10
Volume Number	2
e-ISSN	26316315
Issue Number	Issue 1, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-csr/2/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-csr.2019.0040
Journal	IET Cyber-Systems and Robotics
Publisher	The Institution of Engineering and Technology Zhejiang University Press
Publisher Date	2020-02-20
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Biomimetics Blurred Particles Camera Computer Vision And Image Processing Technique Deep Neural Network Dense Particle Distributions Different Viewing Angles Enhanced Tomographic PIV Reconstruction Estimated Velocity Field Filtering Method Flow Visualisation Flow Visualization And Imaging Ghost Particle High-computational Speed Image Reconstruction Irregular Particle Low Particle Seeding Reconstruction MART Approach Multiple Cameras Multiplicative Algebraic Reconstruction Technique Neural Nets Optical, Image And Video Signal Processing Particle Field Popular Reconstruction Method Reconstruction Accuracy Reconstruction Quality Regenerated Image Spatial Particle Distribution Statistics Symmetric Encoder–decoder Three-dimensional Flow Field Tomographic Particle Image Velocimetry Tomography Trained Neural Network Velocimeters Velocity Measurement
Content Type	Text
Resource Type	Article

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