Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image in-painting in the literature. Errors and noise generated on the colour and grayscale formats of some of the commonly used standard images in the literature were corrected by using Cubic, Kriging, Radial based function and High dimensional model representation approaches and the results were compared using standard image comparison criteria, namely, PSNR (peak signal-to-noise ratio), SSIM (Structural SIMilarity), Mean Square Error (MSE). According to the results obtained from the study, the absolute superiority of the methods against each other was not observed. However, Kriging and RBF interpolation give better results both for numerical data and visual evaluation for image in-painting problems with large area losses.

翻译：内插和内部绘画是图象内部绘画的基本方法之一,用于消除数字图像中出现的不良部分或增加有缺陷的部分,本研究旨在比较文献中图象油漆中图象中使用的内插算法,文献中一些常用标准图象的颜色和灰度格式产生的错误和噪音通过使用立方体、克里吉格、半射线功能和高维模型表达法加以纠正,并且使用标准图像比较标准标准标准比较了结果,即PSNR(信号对音比)、SSIM(结构性SMILIity)、极平方错误(MSE)。根据研究结果,没有观察到这些方法对立的绝对优势。然而,Kriging和RBF的内插图使数字数据和对大面积损失图像的直观评价产生更好的结果。