图像修补的目的是对图像中缺失的区域进行修复，或是将图像中的物体抠去并进行背景填充，以取得融合到难以用肉眼分辨的效果。在图像修补的过程中，较大的结构信息是修补的难点。为此提出了一种快速结构化的图像修补算法，该方法将图像修补分为结构修补与纹理填充两个部分，即在用户指定待修补区域与结构曲线之后，首先定义全局最优化能量函数，并用动态规划与置信度传播的算法将其最小化来完成结构修补；然后对剩余的待修补区域通过按行扫描来进行纹理填充，其中对于边界处的点是使用基于样本的修补算法，而对于待修补区域内部的点，则使用快速的加权Ashikhmin-WL算法，扫描完成后输出修补后的图像；最后实现了一个快速结构化图像修补系统，并给出一些实验结果，从实验结果中可以看到，该方法的修补流程与算法是有实际应用价值的。

Image completion has attracted many researchers these years. The goal of image completion is to repair missing region of images, or to remove objects from images and fill the holes using background information, making it hard to distinguish by eyes. However, to repair huge structure is difficult. We divide the process of image completion into two parts. When the user specified the missing region and structure curves, we first define a global energy function; dynamic programming and belief propagation is used to decide the global minimal cost. This step is also called structure propagation and when it is completed, we scan the region left and implement texture synthesis. For the pixels on boundaries we use exemplar-based algorithm to copy and paste by patch; for the pixels inside the region, we employ a fast weighted Ashikhmin-WL algorithm. At last, the completed image is obtained. We construct a fast structural image completion system and get some results. Experimental results show that our algorithm is useful. Our algorithm will also be extended to video completion in the near future.