|International Journal of Machine Learning and Cybernetics
|Huan Wang*,Qingquan Li,Sen Jia
Ancient murals have been haunted by various problems such as color fading, surface layer turning crisp and even large-area peeling off. Virtually inpainting technologies are widely used to restore these damages. In general, when structure information are blurred or completely missing within a large region, the image inpainting would be more thorny. In this paper, we study mural image inpainting by incorporating structure information collected from the limners guidance or the line drawings, and propose a global and local feature weighted method based on structure guidance to repair the damaged murals of Yulin Grottoes and Mogao Grottoes, Gansu. Unlike traditional methods, a novel sparse representation 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, we apply a global feature patch and local feature patch weighted method to obtain the target patch. Experimental results on damaged murals demonstrate the proposed method outperforms state-of-the-art inpainting methods.