Since the popularity of digital particle image velocimetry technique (DPIV), many PIV image processing algorithms have been proposed. Amongst them, fast Fourier transform (FFT) Cross Correlation, Discrete Window Offset Cross Correlation, Iterative Multigrid Cross Correlation, Iterative Image Deformation Cross Correlation and cross correlation based particle tracking methods are widely used algorithms and have been extensively studied by researchers. All of these algorithms have their advantages and disadvantages in terms of computational load and measurement accuracy. To choose a suitable algorithm, researchers not only need to understand the complex principles of these algorithms, but also need to find out their applicable flow conditions. This could greatly increase work load for PIV users who focus more on flow structure itself instead of PIV algorithms. It is therefore necessary to develop a method which can choose PIV algorithms wisely according to the input PIV images. This paper firstly reviews the development of PIV algorithm with mainly focus on analysing advantages and disadvantages of six widely used algorithms. By using both synthetic and real PIV images, comparative studies are then carried out among these algorithms. The tests give a rate for the performance of the algorithms and provide a parameter to automatically separate pattern match and particle tracking algorithms. Based on qualitative and quantitative analysis, an automated PIV image processing method—SmartPIV is proposed and tested by both synthetic and real PIV images. For all the three test cases, the SmartPIV successfully picks the most suitable algorithm and gives very promising results.
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