# Analysis of Interpolation based Image In-painting Approaches

arXiv: https://arxiv.org/abs/2102.06564 · HTML: https://mlsanity.com/paper/2102.06564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise](https://mlsanity.com/paper/1302.1300/) (2013)
- [Image Inpainting by Kriging Interpolation Technique](https://mlsanity.com/paper/1306.0139/) (2013)
- [Is Hamming distance the only way for matching binary image feature descriptors?](https://mlsanity.com/paper/1512.02355/) (2015)

## Abstract (arXiv)

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.