Nonlocal video denoising, simplification and inpainting using discrete regularization on graphs
Résumé
We present nonlocal algorithms for video denoising, simplification and inpainting based on a generic framework of discrete regularization on graphs. We express video denoising, simplification and inpainting problems using the same variational formulation. The main advantage of this framework is the unification of local and nonlocal approaches for these processing procedures. We take advantage of temporal and spatial redundancies in order to produce high quality results. In this paper, we consider a video sequence as a volume rather than a sequence of frames, and employ algorithms that do not require any motion estimation. For video inpainting, we unify geometric- and texture-synthesis-based approaches. To reduce the computational effort, we propose an optimized method that is faster than the nonlocal approach, while producing equally appealing results.