Image inpainting is the process of restoring a lost or damaged portion of an image. Inpainting of an image that contains texture remains a particularly challenging problem. We aim to propose an algorithm to inpaint a textured image accurately using a single image. The main idea is to segment the given image, based on its texture. In this work, we propose a novel local energy approach, in combination with the k -means algorithm to segment the given image, based on its texture. We use this segmentation result to restrict the search of matching pixels to only-relevant segments. Moreover, we use the entropy-based dissimilarity parameter to find matching pixels, instead of the $$\ell ^2$$ ℓ 2 distance. The restriction of the search area improves the efficiency, and the use of the proposed dissimilarity parameter provides a better way to compare textures, giving improved inpainting for textured images.

中文翻译：

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一种新的基于熵的纹理修复算法

图像修复是恢复图像丢失或损坏部分的过程。修复包含纹理的图像仍然是一个特别具有挑战性的问题。我们的目标是提出一种使用单个图像准确修复纹理图像的算法。主要思想是根据其纹理对给定图像进行分割。在这项工作中，我们提出了一种新颖的局部能量方法，结合 k-means 算法，根据其纹理对给定图像进行分割。我们使用此分割结果将匹配像素的搜索限制为仅相关段。此外，我们使用基于熵的相异参数来查找匹配像素，而不是 $$\ell ^2$$ ℓ 2 距离。搜索区域的限制提高了效率，