Tensor-directed Spatial Patch Blending for Pattern-based Inpainting Methods

International audienceDespite the tremendous advances made in recent years, in the fieldof patch-based image inpainting algorithms, it is not uncommon to still get vis-ible artefacts in the parts of the images that have been resynthetized using thiskind of methods. Mostly, these artifacts take the form of discontinuities betweensynthetized patches which have been copied/pasted in nearby regions, but fromvery different source locations. In this paper, we propose a generic patch blend-ing formalism which aims at strongly reducing this kind of artifacts. To achievethis, we define a tensor-directed anisotropic blending algorithm for neighboringpatches, inspired somehow from what is done by anisotropic smoothing PDE’sfor the classical image regularization problem. Our method has the advantage ofblending/removing incoherent patch data while preserving the significant struc-tures and textures as much as possible. It is really fast to compute, and adaptableto most patch-based inpainting algorithms in order to visually enhance the qualityof the synthetized results