Natural Image Inpainting Based on Exemplar Approach

This paper introduces a novel approach to the problem of image inpainting through the use of nonlocal-means. In traditional inpainting techniques, only local information around the target regions is used to fill in the missing information, which is insufficient in many cases. More recent inpainting techniques based on the concept of exemplar-based synthesis utilize nonlocal information but in a very limited way. In the proposed algorithm, we use nonlocal image information from multiple samples within the image. This paper presents a robust algorithm for exemplar based image inpainting. We focus on the development of a generic priority function to provide robust performance. The Proposed Approach is a very simple inpainting algorithm for reconstruction of small missing and damaged portions of images that is two to three orders of magnitude faster than current methods while producing comparable results.