Astronomical images often have regions with missing or unwanted information, such as bad pixels, bad columns, cosmic rays, masked objects, or residuals from imperfect model subtractions. In certain situations it can be essential, or preferable, to fill in these regions. Most existing methods use low order interpolations for this task. In this paper a method is described that uses the full information that is contained in the pixels just outside masked regions. These edge pixels are extrapolated inwards, using iterative median filtering. This leads to a smoothly varying spatial resolution within the filled-in regions, and ensures seamless transitions between masked pixels and good pixels. Gaps in continuous, narrow features can be reconstructed with high fidelity, even if they are large. The method is implemented in maskfill, an open-source MIT licensed Python package (https://github.com/dokkum/maskfill). Its performance is illustrated with several examples, and compared to several alternative interpolation schemes.

中文翻译：

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一种在天文图像中填充屏蔽数据的稳健且简单的方法

天文图像通常含有丢失或不需要的信息的区域，例如坏像素、坏柱、宇宙射线、遮蔽物体或不完美模型减法的残差。在某些情况下，填充这些区域可能是必要的或更好的。大多数现有方法都使用低阶插值来完成此任务。本文描述了一种方法，该方法使用掩模区域之外的像素中包含的完整信息。使用迭代中值滤波将这些边缘像素向内外推。这导致填充区域内的空间分辨率平滑变化，并确保掩模像素和良好像素之间的无缝过渡。连续、狭窄特征中的间隙即使很大，也可以高保真度地重建。该方法实现于掩模填充，一个 MIT 许可的开源 Python 包（https://github.com/dokkum/maskfill）。通过几个示例说明了其性能，并与几种替代插值方案进行了比较。