Journal of Visualization

, Volume 20, Issue 3, pp 625–638 | Cite as

Hybrid particle image velocimetry with the combination of cross-correlation and optical flow method

  • Zifeng Yang
  • Mark Johnson
Regular Paper


Through a combination of cross-correlation and optical flow method (OFM), a novel technique can benefit from the strengths of each method while mitigating the flaws each individual method contains. The hybrid Particle Image Velocimetry (PIV) method utilizes the state-of-the-art cross-correlation method to account for the relatively large displacements of particles and refine the flow field using the high-resolution analysis of OFM. Image processing techniques such as interpolation, image shifting, and Gaussian filtering are crucial for integrating the cross-correlation technique with optical flow analysis. The accuracy of the hybrid PIV method was validated using standard simulated PIV images that encompassed various parameters encountered in PIV measurements. Each set of images was analyzed by the hybrid method and three other widely used correlation techniques to verify the accuracy. Results confirmed that the hybrid method is consistently more accurate than the other methods in generating the flow vectors, especially near the boundaries. Additionally, for cases dealing with large-sized particles or small displacements, the hybrid PIV method also attains more accurate results.

Graphical Abstract


Hybrid particle image velocimetry Cross correlation Optical flow method 


  1. Adrian RJ (1988) Statistical properties of particle image velocimetry measurements in turbulent flows. In: Adrian RJ et al (eds) Laser anemometry in fluid mechanics III. Springer, New York, pp 115–129Google Scholar
  2. Adrian RJ (2005) Twenty years of particle image velocimetry. Exp Fluids 39:159–169CrossRefGoogle Scholar
  3. Adrian RJ, Westerweed J (2011) Particle image velocimetry. Cambridge University Press, CambridgeGoogle Scholar
  4. Bastiaans RJ (2000) Cross-correlation PIV; theory, implementation and accuracy. Eindhoven University of Technology, EindhovenGoogle Scholar
  5. Bigun J, Granlund GH (1988) Optical flow based on the inertia matrix in the frequency domain. In: Proc. SSAB symposium on picture processing, Lund, SwedenGoogle Scholar
  6. Billy F, David L, Pineau G (2004) Single pixel resolution correlation applied to unsteady flow measurements. Meas Sci Technol 15:1039–1045CrossRefGoogle Scholar
  7. Bruhn A, Weickert J, Schnorr C (2005) Lucas/Kanade Meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vis 61(3):211–231CrossRefGoogle Scholar
  8. Chen X, Zille P, Shao L, Corpetti T (2015) Optical flow for incompressible turbulence motion estimation. Exp Fluids 56(8):1–14Google Scholar
  9. Corpetti T, Memin E, Perez P (2002) Dense estimation of fluid flows. IEEE Trans Pattern Anal Mach Intell 24:365–380CrossRefzbMATHGoogle Scholar
  10. Corpetti T, Heitz D, Arroyo G, Mèmin E, Santa-Cruz A (2006) Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids 40:80–97CrossRefGoogle Scholar
  11. Hart DP (1999) Super-resolution PIV by recursive local-correlation. J Visual Jpn 10:1–10Google Scholar
  12. Hèas P, Mèmin E, Papadakis N, Szantai A (2007) Layered estimation of atmospheric mesoscale dynamics from satellite imagery. IEEE T Geosci Remote 45(12):4087–4104CrossRefGoogle Scholar
  13. Heitz D, Mèmin E, Schnörr C (2010) Variational fluid flow measurements form image sequences: synopsis and perspectives. Exp Fluids 48:369–393CrossRefGoogle Scholar
  14. Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17:185–203CrossRefGoogle Scholar
  15. Hu H, Saga T, Kobayashi T, Okamoto K, Taniguchi N (1998) Evaluation of the Cross correlation method by using PIV standard images. J Visual Jpn 1(1):87–94CrossRefGoogle Scholar
  16. Liu T, Shen L (2008) Fluid flow and optical flow. J Fluid Mech 614:253–291MathSciNetCrossRefzbMATHGoogle Scholar
  17. Liu T, Merat A, Makhmalbaf M, Fajardo C, Merati P (2015) Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images. Exp Fluids 56(8):166CrossRefGoogle Scholar
  18. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proc. seventh international joint conference on artificial intelligence, Vancouver, Canada, pp 674–679Google Scholar
  19. Okamoto K, Nishio S, Saga T, Kobayashi T (2000) Standard images for particle-image velocimetry. Meas Sci Technol 11:685–691CrossRefGoogle Scholar
  20. Quènot GM, Pakleza J, Kowalewski TA (1998) Particle image velocimetry with optical flow. Exp Fluids 25:177–189CrossRefGoogle Scholar
  21. Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle image velocimetry: a practical guide, chapters 3–5, Springer, New YorkGoogle Scholar
  22. Ruhnau P, Kohlberger T, Schnorr C, Nobach H (2005) Variational optical flow estimation for particle image velocimetry. Exp Fluids 38:21–32CrossRefGoogle Scholar
  23. Scarano F, Riethmuller ML (1999) Iterative multigrid approach in PIV image processing with discrete window offset. Exp Fluids 26(6):513–523CrossRefGoogle Scholar
  24. Scarano F, Riethmuller ML (2000) Advances in iterative multigrid PIV image processing. Exp Fluids 29(1):51–60CrossRefGoogle Scholar
  25. Stanislas M, Okamoto K, Kähler C (2003) Main results of the first international PIV challenge. Meas Sci Technol 14:R63–R89CrossRefGoogle Scholar
  26. Stanislas M, Okamoto K, Kähler C, Westerweel J (2005) Main results of the second international PIV challenge. Exp Fluids 39:170–191CrossRefGoogle Scholar
  27. Stanislas M, Okamoto K, Kähler C, Westerweel J, Scarano F (2008) Main results of the third international PIV challenge. Exp Fluids 45:27–71CrossRefGoogle Scholar
  28. Wang B, Cai Z, Shen L, Liu T (2014) An analysis of physics-based optical flow. J Comput Appl Math 276:62–80MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© The Visualization Society of Japan 2017

Authors and Affiliations

  1. 1.Department of Mechanical and Materials EngineeringWright State UniversityDaytonUSA

Personalised recommendations