A dense optical flow registration algorithm based on deep learning

Abstract

Traditional image global registration algorithms are limited in principle and cannot accurately register large depth of field scenes or moving objects. The local registration method based on dense optical flow has the advantage of not being limited by a single transformation matrix, so that a better registration result can be obtained. However, traditional dense optical flow algorithms are limited by large computational complexity and are difficult to achieve real-time estimation, thus limiting their application. In recent years, many dense optical flow algorithms based on deep learning (such as PWC-Net) have emerged, which have achieved the effect of surpassing traditional optical flow algorithms on public datasets and can be estimated in real time. Based on this, this paper proposes an algorithm flow based on deep learning to predict dense optical flow and use it for registration. And a self-built optical flow data set for supervised learning of the network has also been proposed. Using the same network, the registration results of our datasets are better than those of existing datasets.