Tomographic PIV: particles versus blobs

We present an alternative approach to tomographic particle image velocimetry (tomo-PIV) that seeks to recover nearly single voxel particles rather than blobs of extended size. The baseline of our approach is a particle-based representation of image data. An appropriate discretization of this represe...

Full description

Saved in:
Bibliographic Details
Published in:Measurement science & technology 2014-08, Vol.25 (8), p.84002-14
Main Authors: Champagnat, Frédéric, Cornic, Philippe, Cheminet, Adam, Leclaire, Benjamin, Besnerais, Guy Le, Plyer, Aurélien
Format: Article
Language:English
Subjects:
3D PIV
Algebra
Density
imaging
Nucleation
Pixels
point spread function
Reconstruction
Representations
sparsity
Standard deviation
tomography
Velocimetry
volume reconstruction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present an alternative approach to tomographic particle image velocimetry (tomo-PIV) that seeks to recover nearly single voxel particles rather than blobs of extended size. The baseline of our approach is a particle-based representation of image data. An appropriate discretization of this representation yields an original linear forward model with a weight matrix built with specific samples of the system's point spread function (PSF). Such an approach requires only a few voxels to explain the image appearance, therefore it favors much more sparsely reconstructed volumes than classic tomo-PIV. The proposed forward model is general and flexible and can be embedded in a classical multiplicative algebraic reconstruction technique (MART) or a simultaneous multiplicative algebraic reconstruction technique (SMART) inversion procedure. We show, using synthetic PIV images and by way of a large exploration of the generating conditions and a variety of performance metrics, that the model leads to better results than the classical tomo-PIV approach, in particular in the case of seeding densities greater than 0.06 particles per pixel and of PSFs characterized by a standard deviation larger than 0.8 pixels.
ISSN:0957-0233
1361-6501
DOI:10.1088/0957-0233/25/8/084002