Robust Patch Estimation for Exemplar-based Image Inpainting

The exemplar-based image inpainting algorithm consists of two procedures: patch selection and patch estimation. In this paper, we focus on the latter. Conventional exemplar-based approaches cannot fully exploit the information existing in the image, which makes the patch estimation unstable and produces false colors and artifacts. In this paper, we propose a robust patch estimation algorithm for exemplar-based image inpainting. A set of most relevant patches is extracted as reference patches from the known region of the image. A dictionary is learned both globally and adaptively to best represent the target patch, which is then approximated under the assumption that similar patches admit similar decompositions. Experiments show that both quantitative and qualitative improvements are achieved by our method.