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.

中文翻译：

---

使用角度感知补丁匹配的基于样本的图像修复

已经提出了图像修补以根据已知区域的内容来完成丢失的内容。提出了一种基于曲面拟合的先验知识和角度感知贴片匹配的新颖高效的图像修复算法。同时，我们引入了Jaccard相似系数以提高补丁之间的匹配精度。为了减少工作量，我们动态选择目标补丁和源补丁的大小。我们不只是选择一个源补丁，而是通过角度感知旋转策略在全局范围内搜索多个源补丁，以保持结构和纹理的一致性。我们将所提出的方法应用于恢复多个丢失的块和大孔以及对象移除任务。