Particle field deconvolution multiplicative algebraic reconstruction technique for tomographic particle image velocimetry reconstruction

Abstract

Precision tomographic reconstruction is critical for obtaining high-accuracy velocity measurements in tomographic particle image velocimetry. Traditional tomographic reconstruction methods, such as the multiplicative algebraic reconstruction technique (MART), can only be applied at low particle concentrations, limiting the spatial resolution of velocity measurements. In addition, the actual shape of the particles is not reconstructed well due to the limited views. In this study, we propose a novel method named particle field deconvolution MART (Deconv-MART) to repair the shape of actual particles while suppressing ghost particles reconstructed by MART iterations. This method first uses the Gaussian particle shape prior to estimate the convolution kernel obtained by MART reconstruction. Then, the estimated kernel is utilized to deconvolute the particle field and suppress ghost particles based on the prior information of the lower intensity of ghost particles as well as the sparsity of the particle field. Reconstruction fields are estimated with numerical and real experiments, and the results are compared with the results of advanced reconstruction methods. Comparisons of reconstruction demonstrate that the proposed method is effective at suppressing ghost particles and restoring the shape of actual particles. Comparisons of velocity measurements reveal that Deconv-MART has good performance and high measurement accuracy.