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Analysis of interpolation schemes for image deformation methods in PIV

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Abstract

Image deformation methods in particle image velocimetry are becoming more and more accepted by the scientific community but some aspects have not been thoroughly investigated neither theoretically nor with the aid of simulations. A fundamental step in this type of algorithm is reconstruction of the deformed images that requires the use of an interpolation scheme. The aim of this paper is to examine the influence of this aspect on the accuracy of the PIV algorithm. The performance assessment has been conducted using synthetic images and the results show that both the systematic and total errors are strongly influenced by the interpolation scheme used in the reconstruction of the deformed images. Time performances and the influence of particle diameter are also analysed.

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Notes

  1. In many previous works this point is not described in detail, so it is difficult to clearly understand who was the first to propose the two approaches.

  2. Of course the time t is a function of the computer used during the simulation so it should be regarded as a relative measurement.

Abbreviations

BSPLM:

interpolation scheme based on the B-spline of order M

FFT:

fast Fourier transform

FFTM:

interpolation scheme based on the shift theorem of the Fourier transform using M xM points

IDM:

image deformation methods

IDWO:

iterative discrete window offset

IS:

interpolation scheme(s)

PID:

particle image distortion

PIV:

particle image velocimetry

SINCM:

interpolation scheme based on the sinc formula using M xM points

D :

particle diameter, pixels

f :

grey intensity of the first image, dimensionless

g :

grey intensity of the second image, dimensionless

i :

horizontal image coordinate (integer value), pixels

j :

vertical image coordinate (integer value), pixels

l :

horizontal shift, pixels

m :

vertical shift, pixels

N :

number of measurement points, dimensionless

N I :

number of particles per interrogation window, dimensionless

r :

displacement field, pixels

r c :

corrector displacement field, pixels

r w :

displacement field averaged over the interrogation window, pixels

t :

time needed to perform deformation of the images, seconds

ū :

mean measured displacement, pixels

u :

imposed displacement, pixels

u i :

local measured displacement, pixels

W :

interrogation window linear dimension, pixels

x :

horizontal image coordinate, pixels

y :

vertical image coordinate, pixels

\( \overline{\beta } \) :

mean bias error, pixels

β :

bias error, pixels

\( \overline{\sigma } \) :

mean total error, pixels

δ :

total error, pixels

μ :

mean operator

ϕ lm :

cross-correlation coefficient, dimensionless

σ :

random error, pixels

k :

iteration counter, dimensionless

References

  • Gui L, Wereley ST (2002) A correlation-based continuous window-shift technique to reduce the peak-locking effect in digital PIV image evaluation. Exp Fluids 32:506–517

    Article  Google Scholar 

  • Hart DP (2000) Super-resolution PIV by recursive local-correlation. J Visual 3(2):187–194

    Google Scholar 

  • Huang HT, Fiedler HE, Wang JJ (1993) Limitation and improvement of PIV, part 2. Particle image distortion, a novel technique. Exp Fluids 15:263–273

    CAS  Google Scholar 

  • Jambunathan K, Ju XY, Dobbins BN, Ashforth-Frost S (1995) An improved cross correlation technique for particle image velocimetry. Meas Sci Technol 6:507–514

    CAS  Google Scholar 

  • Keane RD, Adrian RJ (1993) Theory of cross correlation analysis of PIV images. In: Nieuwstadt FTM (ed) Flow visualization and image analysis. pp 1–25

  • Lecordier B, Demare D, Vervisch LMJ, Rèveillon J, Trinitè M (2001) Estimation of the accuracy of PIV treatments for turbulent flow studies by direct numerical simulation of multi-phase flow. Meas Sci Technol 12:1382–1391

    Google Scholar 

  • Meunier P, Leweke T (2003) Analysis and treatment of errors due to high velocity gradients in particle image velocimetry. Exp Fluids 35:408–421

    Google Scholar 

  • Nogueira J, Lecuona A, Rodriguez PA (1999) Local field correction PIV: on the increase of accuracy of digital PIV systems. Exp Fluids 27:107–116

    Google Scholar 

  • Nogueira J, Lecuona A, Rodriguez PA (2001) Local field correction PIV, implemented by means of simple algorithms, and multigrid versions. Meas Sci Technol 12:1911–1921

    Google Scholar 

  • Raffel M, Willert CE, Kompenhans J (1998) Particle image velocimetry: a practical guide. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Scarano F (2002) Iterative image deformation methods in PIV. Meas Sci Technol 13:R1–R19

    Article  CAS  Google Scholar 

  • Scarano F (2004) A super-resolution particle image velocimetry interrogation approach by means of velocity second derivatives correlation. Meas Sci Technol 15:475–486

    CAS  Google Scholar 

  • Scarano F, Riethmuller ML (1999) Iterative multigrid approach in PIV image processing with discrete window offset. Exp Fluids 26:513–523

    Article  Google Scholar 

  • Scarano F, Riethmuller ML (2000) Advances in iterative multigrid PIV image processing. Exp Fluids S51–S60

  • Soria J (1996) An investigation of the near wake of a circular cylinder using a video-based digital cross-correlation particle image velocimetry technique. Exp Therm Fluid Sci 12:221–233

    Article  Google Scholar 

  • Unser M (1999) Splines: a perfect fit for signal and image processing. IEEE Signal Proc Mag 16(6):22–38

    Google Scholar 

  • Unser M, Aldroubi A, Eden M (1993a) B-spline signal processing: part I—theory. IEEE T Signal Proces 41(2):821–832

    Google Scholar 

  • Unser M, Aldroubi A, Eden M (1993b) B-spline signal processing: part II—efficient design and applications. IEEE T Signal Proces 41(2):834–848

    Google Scholar 

  • Utami T, Blackwelder RF, Ueno T (1991) A cross-correlation technique for velocity field extraction from particulate visualization. Exp Fluids 10:213–223

    Google Scholar 

  • Wereley ST, Meinhart CD (2001) Second-order accurate particle image velocimetry. Exp Fluids 31:258–268

    Article  Google Scholar 

  • Westerweel J (1993) Digital particle image velocimetry—theory and applications. PhD Thesis, Delft University of Technology, The Netherlands

  • Westerweel J (2000) Theoretical analysis of the measurement precision in particle image velocimetry. Exp Fluids 29:S3–S12

    Article  Google Scholar 

  • Westerweel J, Dabiri D, Gharib M (1997) The effect of a discrete window offset on the accuracy of cross-correlation analysis of digital PIV recordings. Exp Fluids 23:20–28

    Article  Google Scholar 

  • Willert CE, Gharib M (1991) Digital particle image velocimetry. Exp Fluids 10:181–193

    Google Scholar 

  • Yaroslavsky LP (1996) Signal sinc-interpolation: a fast computer algorithm. Bioimaging 4:225–231

    Google Scholar 

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Astarita, T., Cardone, G. Analysis of interpolation schemes for image deformation methods in PIV. Exp Fluids 38, 233–243 (2005). https://doi.org/10.1007/s00348-004-0902-3

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