An image inpainting algorithm using exemplar matching and low-rank sparse prior

Abstract

Image inpainting is a challenging problem with a wide range of applications such as object removal and old photo restoration. The methods based on low-rank sparse prior have been used for regular or nearly regular texture inpainting. However, since such inpainting results do not synthesize the original pixels, they are usually not sharp especially when the area to be recovered is large. One remedy is to use an exemplar-based method. However, it often produces false matches and cannot obtain globally consistent inpainting results. In this paper, we give a new model to promote low rankness and sparsity and solve this model with a recently proposed Riemannian optimization algorithm. Furthermore, we propose a novel two-stage algorithm by integrating the low-rank sparse model with an exemplar-based method. Numerical experiments demonstrate that the proposed low-rank sparsity-based method and the two-stage algorithm achieve encouraging results compared to state-of-the-art image completion algorithms.