SIAM Journal on Imaging Sciences, Volume 13, Issue 4, Page 2140-2168, January 2020.
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.

中文翻译：

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基于非局部特征驱动的基于示例的图像修复方法

SIAM影像科学杂志，第13卷，第4期，第2140-2168页，2020年1月。
我们提出了一种非局部变异图像完成技术，该技术允许在统一框架中同时修复多个结构和纹理。通过使用通用卷积算符作为图像内行为的度量，可以实现几何结构的恢复。这些与基于非局部样本的方法相结合，以在选定的特征域中利用图像的自相似性，并确保纹理的修复。我们还引入了各向异性补丁距离度量，以更好地控制图像中的特征选择，并基于该度量提出非局部能量函数。最后，我们推导了所提出的变分模型的优化算法，并通过各种测试图像实验性地检验了其有效性。