  Mural image inpainting refers to repairing the damage or missing areas in a
mural image to restore the visual appearance. Most existing image-inpainting
methods tend to take a target image as the only input and directly repair the
damage to generate a visually plausible result. These methods obtain high
performance in restoration or completion of some specific objects, e.g., human
face, fabric texture, and printed texts, etc., however, are not suitable for
repairing murals with varied subjects, especially for murals with large damaged
areas. Moreover, due to the discrete colors in paints, mural inpainting may
suffer from apparent color bias as compared to natural image inpainting. To
this end, in this paper, we propose a line drawing guided progressive mural
inpainting method. It divides the inpainting process into two steps: structure
reconstruction and color correction, executed by a structure reconstruction
network (SRN) and a color correction network (CCN), respectively. In the
structure reconstruction, line drawings are used by SRN as a guarantee for
large-scale content authenticity and structural stability. In the color
correction, CCN operates a local color adjustment for missing pixels which
reduces the negative effects of color bias and edge jumping. The proposed
approach is evaluated against the current state-of-the-art image inpainting
methods. Qualitative and quantitative results demonstrate the superiority of
the proposed method in mural image inpainting. The codes and data are available
at {https://github.com/qinnzou/mural-image-inpainting}.

Computer Vision and Pattern Recognition