摘要：Image inpainting aims to obtain a visually plausible image interpolation in a region of the image in which data is missing due to damage or occlusion. Usually, the only available information is the portion of the image outside the inpainting domain. Besides its numerous applications,the inpainting problem is of theoretical interest since its analysis involves an understanding of the self-similarity present in natural images. In this work, we present a detailed description and implementation of three exemplar-based inpainting methods derived from the variational framework introduced by Arias et al.
