In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods.

中文翻译：

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通过基于自适应补丁的概念和等级降低技术使用降维空间进行高效图像修复

在本文中，我们提出了一种使用低维特征空间进行图像修复的新算法。在我们的方法中，首先使用SVD（称为low rank component）从原始空间投影一个低维空间，然后将缺失的像素填充到新空间中。最后，对原始图像进行修复，以便在上一步的基础上通过四叉树考虑自适应块大小。在我们的算法中，目标区域中的缺失像素被估计了两次，一次在低维特征空间中，另一次在原始空间中。值得注意的是，这两个过程都使用基于补丁的想法和降级概念来估计未知像素。与最先进的方法相比，该算法的实验结果显示出更好的一致性。