Edge-Guided Mural Image Inpainting by Integrating Local and Global Information and Multiple Color Spaces

Mural restoration holds significant importance for various reasons from cultural, historical, and artistic aspects. Although significant progress has been made in natural image restoration in recent years, mural image restoration remains a challenging problem due to its differences in structure, color, and style from natural images. Motivated by the structural and color distribution characteristics of mural images, we propose a novel two-stage edge-guided mural in-painting model that perceives global and local information through a Transformer-Multi-scale CNN block (TMC) and learns informative color cues by introducing an extra YUV color space. Extensive experiments on Mural and CelebA-HQ datasets show that our proposed approach outperforms the other state-of-the-art baselines in both qualitative and quantitative evaluations.