Exemplar-based color image inpainting: a fractional gradient function approach

Abstract

Image inpainting is the art of recovering a plausible image from images which are generally incomplete due to various factors, including degradation due to ageing, damage due to wear and tear and missing image details due to occlusion. In such situations, there is a need to predict the missing image information without introducing undesirable artifacts. Original contribution in this direction is due to a seminal paper by Criminisi et al. This has led to a number of novel contributions in terms of patch filling prioritization and associated metrics to measure color and structure. In this paper, we propose a fast and simple technique based on a novel gradient function and its generalization via fractional derivatives to evaluate the filling order prioritization. Results demonstrate superior and robust performance over all the recent advances in the domain of exemplar-based methods quoted in the literature.