Towards unifying diffusion and exemplar-based inpainting

A novel framework for image inpainting is proposed, relying on graph-based diffusion processes. Depending on the construction of the graph, both flow-based and exemplar-based inpainting methods can be implemented by the same equations, hence providing a unique framework for geometry and texture-based approaches to inpainting. Furthermore, the use of a variational framework allows to overcome the usual sensitivity of exemplar-based methods to the heuristic issues by providing an evolution criterion. The use of graphs also makes our framework more flexible than former nonlocal variational formulations, allowing for example to mix spatial and non-local constraints and to use a data term to provide smoother blending between the initial image and the result.