Inpainting of Dunhuang Murals by Sparsely Modeling the Texture Similarity and Structure Continuity

Abstract

Ancient mural paintings often suffer from damage such as color degradation, pigment peeling, and even large-area shedding. Image inpainting techniques are widely used to virtually repair these damages. Generally, the inpainting task can be very challenging when structures are totally missing within a large area. In this article, we study mural image inpainting by incorporating structure information collected from line drawings, and propose a line-drawings-guided inpainting algorithm for repairing the damaged murals of Mogao Grottoes, Dunhuang. Unlike traditional methods that use one single patch to inpaint the target area, the proposed method constructs the target patch with a linear combination of multiple candidate patches. These candidate patches are selected by a sparse model, where two special constraints have been introduced to guarantee the texture similarity and structure continuity. Experimental results demonstrate the effectiveness of the proposed method.