This paper proposes a global uniform and local continuity repair method for mural image inpainting. It uses the relationship between linear system and image repair to construct the similarity-preserving overcomplete dictionary with global weighted feature. Meanwhile, a novel sparse repair model with elastic net regularization based on similarity-preserving overcomplete dictionary is formulated to enhance the global feature consistency, and then an estimated method of neighborhood similarity is presented to guarantee local feature consistency, finally, a global fea? ture patch and local feature patch weighted method are applied to obtain the target patch. Experimental results on damaged murals demonstrate the proposed method outperforms state-of-the-art inpainting methods.