The article discusses application of Robust Optical Flow Estimation for increasing of Particle Image Velocimetry measurement resolution. Nowadays, one of the promising approaches for increasing the performance of the PIV systems is application of the Optical Flow Estimation for image analysis. Nevertheless, some of the OF implementations do not perform well in case of motion discontinues typically occurring in the PIV images. The purpose of this study is to validate the performance of the Robust Optical Flow Estimation. The tests were performed on simulated images of vortex flow and the results were compared with displacement fields calculated with the typical correlation PIV algorithm. The velocity for high and medium particle concentration was similar for Optical Flow and PIV-like analysis. Furthermore, the performance of the robust optical flow framework was tested with images corrupted with blurs and occlusions. The tests proved good performance of proposed analysis in case of non-Gaussian sources of measurement errors. The robust estimation framework performed well in the case of common image artefacts and proved to be a promising method for precise PIV flow measurements. The presented approach can be useful in development hybrid OF-PIV post processing software aimed for high-resolution measurements and provide a help in designing of experimental investigation of microscale fluid flow phenomena.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Anderson J. D. Fundamentals of Aerodynamics McGraw-Hill’s New York 2004.
 Billy F. David L. Pineau G. Single pixel resolution correlation applied to unsteady flow measurements Measurement Science and Technology Vol. 15 pp. 1039-1045 2004.
 Black M. J. Anandan P. A framework for the robust estimation of optical Flow Fourth International Conf. on Computer Vision ICCV-93 Berlin 1993.
 Black M. J. Anandan P. The robust estimation of multiple motions: parametric and piecewise-smooth flow fields Computer Vision and Image Understanding Vol. 63 No. 1 pp. 74-104 1994.
 Cao X. Liu J. Jiang N. Chen Q. Particle image velocimetry measurement of indoor airflow field: A review of the technologies and applications Energy and Buildings Vol. 69 pp. 367-380 2014.
 Cheng Y. Torregrosa M. M. Villegas A. Diez F. J. Time resolved scanning PIV measurements at fine scales in a turbulent jet International Journal of Heat and Fluid Flow No. 32 pp. 708-718 2011.
 Garcia D. A fast all-in-one method for automated post-processing Experiments in Fluids Vol. 50 pp. 1247-1259 2011.
 Giepman R. H. M. Schrijer F. F. J. van Oudheusden B. W. High resolution PIV measurements of a transitional shock Experiments in Fluids Vol. 56 pp. 113-133 2015.
 Heas P. Heitz D. Memin E. Mininni P. Mulitscale regularization based on turbulnent kinetic energy decay for PIV estimations with high spatial regularization 8th Int. Symposium on Particle Image Velocimetry (PIV09) Melbourne 2009.
 Heitz D. Memin E. Schnorr C. Variational fluid flow measurements from image sequences: synopsis and perspectives Experiments in Fluids Vol. 48 No. 3 pp. 369-393 2010.
 Horn B. K. P. Schunck B. G. Determining optical flow Artificial Intelligence Vol. 17 pp. 185-203 1981.
 Kahler C. J. Scholz U. Transonic jet analysis using long-distance micro PIV Proceedings of 12th International Symposium On Flow Visualization Gottingen 2006
 Kahler C. J. Scholz U. Ortmanns J. Wall-shear-stress and near wall turbulence measurements up to single pixel resolution by means of long-distance micro-PIV Experiments in Fluids Vol. 41 pp. 327-341 2006.
 Lewis J. P. Fast normalized cross-correlation Vision interface Vol. 5 pp. 120-123 1995.
 Mamla P. Galinski C. Basic induced drag study of the joined-wing aircraft AIAA Journal of Aircraft Vol. 46 pp. 1438–1440 2009.
 Raffel M. Willert C. E. Wereley S. T. Kompenhans J. Particle Image Velocimetry Springer-Verlag Berlin 2007.
 Stryczniewicz W. Development of Particle Imagine Velocimetry Algorithm Problems of Mechatronics Vol. 9 pp. 41-54 2012.
 Sun D. Roth S. Black M. J. Secrets of optical flow estimation and their principles IEEE Conference on Computer Vision and Pattern Recognition San Francisco 2010.
 Thielicke W. Stamhuis E. J. PIVlab – Towards User-friendly Affordable and Accurate Digital Particle Image Velocimetry in MATLAB Journal of Open Research Software Vol. 2 No. 1 pp. 2-10 2014.
 Urban J. M. Zloczewska A. Stryczniewicz W. Jönsson-Niedziolka M. Enzymatic oxygen reduction under quiescent conditions – The importance of convection Electrochemistry Communications Vol. 34 pp. 94-97 2013.
 Westerweel J. Geelhoed P. F. Lindken R. Single-pixel resolution ensemble correlation for micro-PIV applications Experiments in Fluids Vol. 37 pp. 375-384 2004.