  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.
