Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning
Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical p...
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Published in | Advances in aerodynamics Vol. 3; no. 1; pp. 1 - 14 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Singapore
Springer Nature Singapore
23.09.2021
Springer Nature B.V SpringerOpen |
Subjects | |
Online Access | Get full text |
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Summary: | Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical particle reconstruction method based on a convolutional neural network (CNN) with geometry-informed features is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique (ART) based methods. Compared with available ART-based algorithms, the novel technique makes significant improvements in terms of reconstruction quality, robustness to noise, and at least an order of magnitude faster in the offline stage. |
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ISSN: | 2524-6992 2524-6992 |
DOI: | 10.1186/s42774-021-00087-6 |