Context Aware Exemplar-based Image Inpainting using Irregular Patches

Abstract

We propose a new exemplar-based image inpainting method in this paper. Our method is based on the Criminisi pipeline. We focused on three main stages of the pipeline; calculation of priorities, construction of patches, and the search for the best match. To assign a high priority to patches constructed from the edge pixels, we use the ability of segmentation algorithms to divide an image into different texture blocks. The patches built from pixels located at the border between several texture blocks receive a high priority. Unlike most patch-based image inpainting methods which use regular patches (rectangle, square), the shape and size of our patches depend on the textural composition around the original pixel. The patches are built using a region growing principle in the different texture blocs around the original pixel. The search for the best match is done contextually. We search for the best match of the patch with the highest priority in a similar environment to its neighborhood around the target zone. The method is simple and easy to implement. The experiments show that our method obtains more plausible results than the basic method of Criminisi and its improved version Amoeba in most cases.