Deep dual recurrence optical flow learning for time-resolved particle image velocimetry

Motion fields estimated from image data have been widely used in physics and engineering. Time-resolved particle image velocimetry (TR-PIV) is considered as an advanced flow visualization technique that measures multi-frame velocity fields from successive images. Contrary to conventional PIV, TR-PIV essentially estimates a velocity field video that provides both temporal and spatial information. However, performing TR-PIV with high computational efficiency and high computational accuracy is still a challenge for current algorithms. To solve these problems, we put forward a novel deep learning network named Deep-TRPIV in this study, to effectively estimate fluid motions from multi-frame particle images in an end-to-end manner. First, based on particle image data, we modify the optical flow model known as recurrent all-pairs field transforms that iteratively updates flow fields through a convolutional gated recurrent unit. Second, we specifically design a temporal recurrent network architecture based on this optical flow model by conveying features and flow information from previous frame. When N successive images are fed, the network can efficiently estimate N - 1 motion fields. Moreover, we generate a dataset containing multi-frame particle images and true fluid motions to train the network supervised. Eventually, we conduct extensive experiments on synthetic and experimental data to evaluate the performance of the proposed model. Experimental evaluation results demonstrate that our proposed approach achieves high accuracy and computational efficiency, compared with classical approaches and related deep learning models.