Optical flow estimation with confidence measures for super-resolution based on recursive robust total least squares

In this paper we propose a novel optical flow estimation method accompanied by confidence measures. Our main goal is fast and highly accurate motion estimation in regions where information is available and a confidence measure which identifies these regions. Therefore we extend the structure tensor method to robust recursive total least squares (RRTLS) and run it on a GPU for real-time processing. Based on a coarse-to-fine framework we propagate not only the motion estimates to finer scales but also the covariance matrices, which may be used as confidence measures. Experiments on synthetic data show the benefits of our approach. We applied the RRTLS framework to a real-time super-resolution method for deforming objects which incorporates the confidence measures and demonstrates that propagating the covariances through the pyramid improves super-resolution results.