Sparse modeling based image inpainting with local similarity constraint

In this paper, we propose an efficient exemplar-based inpainting algorithm via sparse modeling and local similarity constraint. The inpainting procedure contains two steps: calculating the filling order and reconstructing the target patch. The filling order is decided by patch priority, which privileges the patch located at edge or corner. The target patch is then estimated by a combination of candidate patches. In the proposed method, three regularization terms are introduced to improve the patch reconstruction step. The first term ensures the compatibility between the target patch and the estimated one. The second term assigns larger combination coefficients for the candidate patches which are most similar with the target patch. The third term penalties the combination coefficients for the outliers in the candidate patches. Finally, the three regularization terms are incorporated into a unified sparse representation framework for reconstructing the target patch. Experiments show that the proposed algorithm can effectively fill in missing pixels in a visually plausible way.