Reconstructing Optical Flow Fields by Motion Inpainting

An edge-sensitive variational approach for the restoration of optical flow fields is presented. Real world optical flow fields are frequently corrupted by noise, reflection artifacts or missing local information. Still, applications may require dense motion fields. In this paper, we pick up image inpainting methodology to restore motion fields, which have been extracted from image sequences based on a statistical hypothesis test on neighboring flow vectors. A motion field inpainting model is presented, which takes into account additional information from the image sequence to improve the reconstruction result. The underlying functional directly combines motion and image information and allows to control the impact of image edges on the motion field reconstruction. In fact, in case of jumps of the motion field, where the jump set coincides with an edge set of the underlying image intensity, an anisotropic TV-type functional acts as a prior in the inpainting model. We compare the resulting image guided motion inpainting algorithm to diffusion and standard TV inpainting methods.