Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional projections is an underdetermined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by optimization methods. In order to obtain a better quality particle field for Tomographic PIV, in the current work, a practical particle reconstruction method based on convolutional neural network (CNN) is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution from 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. It can effectively eliminate ghost particles and restore the shape of particles more accurately, and is at least an order of magnitude faster with dense particle concentration.