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Poor-Contrast Particle Image Processing in Microscale Mixing

[+] Author Affiliations
F. Gökhan Ergin, Bo Beltoft Watz

Dantec Dynamics A/S, Skovlunde, Denmark

Kaspars Erglis, Andrejs Cebers

University of Latvia, Riga, Latvia

Paper No. ESDA2010-24900, pp. 649-653; 5 pages
  • ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis
  • ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 5
  • Istanbul, Turkey, July 12–14, 2010
  • Conference Sponsors: International
  • ISBN: 978-0-7918-4919-4 | eISBN: 978-0-7918-3877-8
  • Copyright © 2010 by ASME


Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is sufficiently poor, the noise peaks cast a shadow on the true correlation peak, producing erroneous velocity vectors. This work aims to demonstrate that two image pre-processing techniques — local contrast normalization and Difference of Gaussian (DoG) filter — improve the correlation results significantly in poor-contrast regions.

Copyright © 2010 by ASME



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