ABSTRACT In order to retrieve large scale damaged image with rich geometry structure and texture information, the novel image inpainting algorithm based on the neighbourhood reference priority can not only maintain image character but also improve image inpainting speed has been proposed in the paper. The problems of an image inpainting process can be translated into the best sample searching process. First, the extracting structure information of image and the dividing sample region into sub-regions server. Second, in order to adjust the neglect of structure of matching method named SSD, introducing structure symmetry matching constraint into matching method, it avoids matching mistakenly and improves sample matching precision and searching efficiency. Then, improving priority equations by bringing in structure weight and confidence, highlighted the effect of structure to inpainting sequence. Finally, computing priority of four-domains neighbour by computation overlapping information between object patch and sample patch, so that referring to secure information of four-domains neighbour, to prune sample dataset and search optimal sample. The experimental results have demonstrated that the proposed algorithm can overcome problems like texture blurring and structure dislocations and so on, the PSNR of the improved algorithm has been increased by 0.5–1 dB comparing other contrast methods while speeding up the image inpainting process, recovered image is much continuous for visuality. Meanwhile, it can recover efficiently common damage images and be more pervasive.

中文翻译：

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基于四域修剪样本的图像修复算法

摘要 为了检索具有丰富几何结构和纹理信息的大尺度受损图像，提出了一种基于邻域参考优先级的图像修复新算法，既能保持图像特征，又能提高图像修复速度。图像修复过程的问题可以转化为最佳样本搜索过程。首先，提取图像的结构信息并将样本区域划分为子区域服务器。其次，为了调整匹配方法SSD对结构的忽视，在匹配方法中引入结构对称匹配约束，避免错误匹配，提高样本匹配精度和搜索效率。然后，通过引入结构权重和置信度来改进优先级方程，突出了结构对修复序列的影响。最后，通过计算对象补丁和样本补丁之间的重叠信息计算四域邻居的优先级，从而参考四域邻居的安全信息，修剪样本数据集并搜索最优样本。实验结果表明，该算法能够克服纹理模糊、结构错位等问题，改进算法的PSNR与其他对比方法相比提高了0.5-1 dB，同时加快了图像修复过程，恢复了图像在视觉上非常连续。同时，它可以有效地恢复常见的损坏图像，并且更具普遍性。通过计算对象补丁和样本补丁之间的重叠信息计算四域邻居的优先级，以便参考四域邻居的安全信息，修剪样本数据集并搜索最佳样本。实验结果表明，该算法能够克服纹理模糊、结构错位等问题，改进算法的PSNR与其他对比方法相比提高了0.5-1 dB，同时加快了图像修复过程，恢复了图像在视觉上非常连续。同时，它可以有效地恢复常见的损坏图像，并且更具普遍性。通过计算对象补丁和样本补丁之间的重叠信息计算四域邻居的优先级，以便参考四域邻居的安全信息，修剪样本数据集并搜索最佳样本。实验结果表明，该算法能够克服纹理模糊、结构错位等问题，改进算法的PSNR与其他对比方法相比提高了0.5-1 dB，同时加快了图像修复过程，恢复了图像在视觉上非常连续。同时，它可以有效地恢复常见的损坏图像，并且更具普遍性。实验结果表明，该算法能够克服纹理模糊、结构错位等问题，改进算法的PSNR与其他对比方法相比提高了0.5-1 dB，同时加快了图像修复过程，恢复了图像在视觉上非常连续。同时，它可以有效地恢复常见的损坏图像，并且更具普遍性。实验结果表明，该算法能够克服纹理模糊、结构错位等问题，改进算法的PSNR与其他对比方法相比提高了0.5-1 dB，同时加快了图像修复过程，恢复了图像在视觉上非常连续。同时，它可以有效地恢复常见的损坏图像，并且更具普遍性。