基于双十字曲率驱动扩散模型的图像修复算法

当前各种基于曲率驱动扩散(CDD)模型的图像修复算法在修复待修复点时均只利用了其邻域中的4个点的参考信息，使修复后的图像边缘过渡不自然且修复精度不够高。针对以上问题，提出了基于双十字CDD的图像修复算法。该算法在充分利用原始CDD算法中4个邻域点的参考信息得到待修复点的修复像素值的基础上，再利用新引入的4个点的参考信息得到一个新的修复像素值，并将这两个修复像素值进行加权平均得到最终的修复像素值。最后，将提出的算法和原始的CDD算法以及改进的CDD算法用于实例验证，其结果表明，新提出的算法在不增加算法时间复杂度的条件下，使得图像边缘过渡更加自然，修复精度得到了有效提高。

Image inpainting algorithm based on double-cross curvature-driven diffusion model

Currently, various image inpainting algorithms based on Curvature-Driven Diffusion (CDD) model only make use of the reference information of four neighborhood pixels. Therefore, they cannot keep shape edges and their inpainting precisions high enough. To conquer these difficulties, the image inpainting algorithm based on double-cross CDD was presented, in which the reference information for damaged pixel was extended from four into eight neighborhood pixels. Firstly, one inpainting value for damaged pixel could obtain from the reference information of four neighborhood pixels using the original CDD algorithm. Secondly, another new inpainting value was computed with the newly introduced four neighborhood pixels. Finally, the final inpainting value was a weighted mean of the above-mentioned two inpainting computational value. The proposed method, original CDD algorithm and its improved editions were implemented and compared in the experiments. The experimental results show that the proposed algorithm can effectively improve the inpainting precision and keep shape edges without increasing time complexity.