Analysis of Interpolation based Image In-painting Approaches

TL;DR

This paper compares various interpolation algorithms for image in-painting, evaluating their effectiveness in restoring images with large missing areas using standard metrics.

Contribution

It provides a comparative analysis of Cubic, Kriging, RBF, and HDMR interpolation methods for image in-painting, highlighting their relative performance.

Findings

Kriging and RBF interpolation outperform others in large-area in-painting.

No single method is superior in all cases.

Evaluation based on PSNR, SSIM, and MSE confirms results.

Abstract

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.