Geometric and texture inpainting based on discrete regularization on graphs

We present an inpainting method for images and videos based on nonlocal discrete p-Laplace regularization on weighted graphs. Our work has the advantage of unifying local geometric methods and nonlocal exemplar-based ones in the same framework. Our image inpainting benefits from local and nonlocal regularities within the image. In addition to that, our video inpainting exploits temporal and spatial redundancies in order to obtain high quality results by considering a video sequence as a volume and not as a sequence of still frames. However, our method does not employ any motion estimation for video inpainting. Experiments demonstrate that our nonlocal method outperforms the local one by completing missing data with finer and more consistent details for textured and non-textured images and videos.