Non-local segmentation and inpaiting
This article introduces a new variational image segmentation method that makes use of non-local comparisons between pairs of patches in the image and is robust to missing data (e.g. damaged pixels or large missing regions). The resulting segmentation is at the heart of a novel inpainting algorithm that also uses a non-local regularization. This segmentation and inpainting approach only requires a local homogeneity of the features inside and outside the region to be segmented. In contrast to existing region-based segmentation methods, it allows us to segment regions with smoothly varying intensity as well as multiple objects with different intensities. This comparison principle is also less sensitive to initialization than edge-based approaches.