Fast and accurate PIV computation using highly parallel iterative correlation maximization

Our contribution deals with fast computation of dense two-component (2C) PIV vector fields using Graphics Processing Units (GPUs). We show that iterative gradient-based cross-correlation optimization is an accurate and efficient alternative to multi-pass processing with FFT-based cross-correlation. Density is meant here from the sampling point of view (we obtain one vector per pixel), since the presented algorithm, folki, naturally performs fast correlation optimization over interrogation windows with maximal overlap. The processing of 5 image pairs (1,376 × 1,040 each) is achieved in less than a second on a NVIDIA Tesla C1060 GPU. Various tests on synthetic and experimental images, including a dataset of the 2nd PIV challenge, show that the accuracy of folki is found comparable to that of state-of-the-art FFT-based commercial softwares, while being 50 times faster.