The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the visual perception of an image and in classical recognition and robotics problems in order to remove irrelevant information from an image. Modern methods of impainting use neural networks. However, these approaches have drawbacks that do not allow the use of these algorithms in the practice of computer vision. In this article, we propose to use a nonlocal means (NLM) filter, which has proven itself to be excellent in image noise reduction tasks. The basis of our motivation is the fact that the goal of the NLM, like any method for recovering images distorted by noise, is to minimize the distance (or error by some criterion) between the original image and the reconstructed one. The result of computer experiments showed that the proposed method of impainting is superior to other methods according to peak signal-to-noise ratio (PSNR) criterion. The effectiveness of the proposed filter is also shown with the help of illustrations to the article, so that the reader can compare the quality of different processing options visually.

使用来自未失真区域的纹理填充被伪影失真的图像区域的需要称为修复。Impainting 既可用于改善图像的视觉感知，也可用于经典识别和机器人问题，以从图像中删除不相关的信息。现代的 impainting 方法使用神经网络。然而，这些方法具有不允许在计算机视觉实践中使用这些算法的缺点。在本文中，我们建议使用非局部均值 (NLM) 滤波器，该滤波器已被证明在图像降噪任务中表现出色。我们动机的基础是这样一个事实，即 NLM 的目标，就像任何恢复被噪声扭曲的图像的方法一样，是最小化原始图像和重建图像之间的距离（或某些标准的误差）。计算机实验结果表明，根据峰值信噪比（PSNR）标准，所提出的修复方法优于其他方法。所提出的过滤器的有效性也通过文章的插图显示出来，以便读者可以直观地比较不同处理选项的质量。