Spatial filtering improved tomographic PIV

Tomographic reconstruction accuracy is of fundamental importance to obtain reliable three-dimensional three-components velocity field measurements when implementing tomographic particle image velocimetry. Algebraic methods (Herman and Lent 1976) are quite well established to handle the problem in case of high spatial frequency spots on a dark background imaged by a limited number of simultaneous views; however, their efficacy is limited in case of dense distributions to be reconstructed. In the present work, an easy implementable modified version of the commonly used multiplicative algebraic reconstruction technique is proposed, allowing a remarkable improvement of the tomographic reconstruction quality only slightly increasing the computational cost. The technique is based on artificial diffusion applied by Gaussian smoothing after each iteration of the reconstruction procedure. Numerical simulations show that the increase in the reconstruction quality leads to a significant reduction of the modulation effects in the velocity measurement due to the coherent ghost particles motion. An experimental application in fractal grid turbulence highlights an improvement of the signal strength and a reduction of the uncertainty in the velocity measurement.