<p>The murals in Shaanxi temples and monasteries, with their long history and diverse styles, are invaluable yet non-renewable cultural heritage. However, prolonged environmental exposure has led to severe damage, including cracking, mold growth, and large-scale detachment, creating an urgent need for restoration. Traditional restoration methods struggle with reconstructing complex structures and patterns due to their neglect of the murals’ global structure. To address this, we propose a novel diffusion model guided by global low-rank structure for mural restoration. By leveraging the inherent low-rank prior of mural images, our model explicitly captures non-local similarities within murals. To enhance computational efficiency, we incorporate orthogonal Tucker decomposition, reducing the complexity of low-rank solutions. Comprehensive experiments and ablation studies validate the effectiveness of the low-rank prior, demonstrating that our model achieves state-of-the-art performance and provides significant advancements in digital restoration of ancient murals.</p>