Exemplar-based image inpainting using angle-aware patch matching

Image inpainting has been presented to complete missing content according to the content of the known region. This paper proposes a novel and efficient algorithm for image inpainting based on a surface fitting as the prior knowledge and an angle-aware patch matching. Meanwhile, we introduce a Jaccard similarity coefficient to advance the matching precision between patches. And to decrease the workload, we select the sizes of target patches and source patches dynamically. Instead of just selecting one source patch, we search for multiple source patches globally by the angle-aware rotation strategy to maintain the consistency of the structures and textures. We apply the proposed method to restore multiple missing blocks and large holes as well as object removal tasks. Experimental results demonstrate that the proposed method outperforms many current state-of-the-art methods in patch matching and structure completion.