title: Hierarchical super-resolution-based inpainting.
abstract: This paper introduces a novel framework for examplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A hierarchical super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with a loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method. Results are compared to five state-of-the-art inpainting methods.
authors: Institut de Recherche en Informatique et Systèmes Aléatoires, University of Rennes 1, Rennes, France. olemeur@irisa.fr
authors: Olivier
authors: O
authors: Le Meur
authors: Mounira
authors: M
authors: Ebdelli
authors: Christine
authors: C
authors: Guillemot