Microfluidics and Nanofluidics

, Volume 14, Issue 3–4, pp 431–444 | Cite as

Improved accuracy of time-resolved micro-Particle Image Velocimetry using phase-correlation and confocal microscopy

  • Jaime S. Raben
  • Steven A. Klein
  • Jonathan D. Posner
  • Pavlos P. Vlachos
Research Paper


Micro-Particle Image Velocimetry (μPIV) measurements often suffer from poor image quality because of volume illumination effects, out of focus particles, and low seeding densities. As a result, measurements are typically ensemble averaged in time to improve the signal-to-noise ratio (SNR) of the resulting cross correlations. To achieve reliable, time-accurate μPIV measurements we need to improve the SNR of the recorded images and/or the SNR treatment of the resulting cross correlations. In this paper, we improve image quality and cross correlation SNR by comparing the use of confocal microscopy with spectral filtering. Steady-state spatiotemporally resolved data from widefield and confocal μPIV experiments were used and cross correlations were performed using standard techniques and the Robust Phase Correlation (RPC) method that employs a PIV-optimized spectral filter on the cross-correlation planes. The accuracy improvements were assessed by comparison against the time-averaged ensemble cross correlation, which currently represents the most accurate and accepted approach for steady-state μPIV measurements. Results show 24.77 % erroneous vectors for two-pass standard cross correlation with widefield imaging, which was reduced to 9.08 % erroneous vectors when using the RPC and confocal imaging. Furthermore, a 59.2 % reduction of error referenced to the ensemble correlation was observed when using RPC with confocal imaging over baseline cases. Improvements seen for RPC and confocal cases result from synergistically improving the correlation signal-to-noise ratio, resulting in correlation planes with sharper primary peaks and lower background levels.


Micro-PIV Phase correlation RPC Confocal microscopy 



Confocal imaging


Discrete window offset


Generalized cross correlation


Image signal-to-noise ratio


Standard cross correlation C 12


Widefield imaging

List of symbols


Correlation signal-to-noise ratio based on the ratio between the first and second highest peaks

Ensemble correlation

Fields resulting from averaging correlations in time at each vector location rather than averaging instantaneous velocity measurements in time


Error with respect to the ensemble in pixels (combined bias and RMS with respect to the ensemble)


Phase transform filter \( W(k) = \frac{1}{{|C_{12} (k)|}} \)


Robust phase correlation filter \( \frac{{{\text{SNR}}(k)}}{{\left| {C_{12} (k)} \right|}} \)

\( u_{ij}^{\text{ens}} ,\;v_{ij}^{\text{ens}} \)

Streamwise and spanwise velocity fields resulting from ensemble correlations in units of pixels/frame-pair

\( u_{ij} ,\;v_{ij} \)

Instantaneous streamwise and spanwise velocity fields in units of pixels/frame-pair

\( \overline{u}_{ij} ,\;\overline{v}_{ij} \)

Time-averaged velocity

\( \overline{u}_{j} ,\;\overline{v}_{j} \)

Time and streamwise averaged velocity profiles for streamwise and spanwise components in units of pixels/frame-pair

\( \sigma_{j}^{{\overline{u} }} ,\;\sigma_{j}^{{\overline{v} }} \)

Standard deviation of time-averaged fields at each spanwise location



Support for this work was provided by NSF CAREER award (CBET-0747917).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jaime S. Raben
    • 1
  • Steven A. Klein
    • 2
    • 5
  • Jonathan D. Posner
    • 3
  • Pavlos P. Vlachos
    • 4
  1. 1.School of Biomedical Engineering and SciencesVirginia TechBlacksburgUSA
  2. 2.Department of Mechanical EngineeringArizona State UniversityChandlerUSA
  3. 3.Department of Mechanical Engineering, Chemical EngineeringUniversity of WashingtonSeattleUSA
  4. 4.Department of Mechanical EngineeringVirginia TechBlacksburgUSA
  5. 5.IntelChandlerUSA

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