Experiments in Fluids

, Volume 44, Issue 6, pp 1015–1026 | Cite as

A color-coded backlighted defocusing digital particle image velocimetry system

  • Wei-Hsin Tien
  • Patrick Kartes
  • Toru Yamasaki
  • Dana DabiriEmail author


Defocusing digital particle image velocimetry (DDPIV), as a true three-dimensional (3D) measurement technique, allows for the measurement of 3D velocities within a volume. Initially designed using a single CCD and 3-pinhole mask (Willert and Gharib in Exp Fluids 12:353–358, 1992), it has evolved into a multi-camera system in order to overcome the limitations of image saturation due to multiple exposures of each particle. In order to still use a single camera and overcome this limitation, we have modified the original single CCD implementation by placing different color filters over each pinhole, thus color-coding each pinhole exposure, and using a 3-CCD color camera for image acquisition. Due to the pinhole mask, there exists the problem of a significant lack of illumination in a conventional lighting setup, which we have solved by backlighting the field-of-view and seeding the flow with black particles. This produces images with a white background superimposed with colored triple exposures of each particle. A color space linear transformation is used to allow for accurate identification of each pinhole exposure when the color filters’ spectrum does not match those of the 3-CCD color camera. Because the imaging is performed with a multi-element lens instead of a single-element lens, an effective pinhole separation, d e, is defined when using a pinhole mask within a multi-element lens. Calibration results of the system with and without fluid are performed and compared, and a correction of the effective pinhole separation, d e, due to refraction through multiple surfaces is proposed. Uncertainty analyses are also performed, and the technique is successfully applied to a buoyancy-driven flow, where a 3D velocity field is extracted.


Test Section Particle Image Color Filter Lens Plane Color Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank professor Gamal Khalil from the UW Chemistry department for his help in dying the polystyrene particles. This work is supported by the National Institute of Health (SRS 20309/SAP 1004717).


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Wei-Hsin Tien
    • 1
  • Patrick Kartes
    • 1
  • Toru Yamasaki
    • 2
  • Dana Dabiri
    • 1
    Email author
  1. 1.Department of Aeronautics and AstronauticsUniversity of WashingtonSeattleUSA
  2. 2.Software Cradle Co., Ltd.OsakaJapan

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