ABSTRACT
Interpolation and internal painting are among 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 greyscale formats of some of the commonly used standard images in the literature were corrected by using cubic, Kriging, radial-based function (RBF), 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), and mean square error (MSE). According to the results obtained from the study, absolute superiority of the methods against each other was not observed. However, Kriging and RBF interpolation give better results for both numerical data and visual evaluation for image in-painting problems with large area losses.
