Tensor-Directed Spatial Patch Blending for Pattern-Based Inpainting Methods

Abstract

Abstract. Despite the tremendous advances made in recent years, in the field of patch-based image inpainting algorithms, it is not uncommon to still get visible artefacts in the parts of the images that have been resynthetized using this kind of methods. Mostly, these artifacts take the form of discontinuities between synthetized patches which have been copied/pasted in nearby regions, but from very different source locations. In this paper, we propose a generic patch blending formalism which aims at strongly reducing this kind of artifacts. To achieve this, we define a tensor-directed anisotropic blending algorithm for neighboring patches, inspired somehow from what is done by anisotropic smoothing PDE's for the classical image regularization problem. Our method has the advantage of blending/removing incoherent patch data while preserving the significant structures and textures as much as possible. It is really fast to compute, and adaptable to most patch-based inpainting algorithms in order to visually enhance the quality of the synthetized results.