Diffusion-based heterogeneous network for ancient mural restoration

Abstract Ancient murals face significant degradation due to environmental and human factors. Traditional restoration is labor-intensive and inconsistent, while recent advancements in deep learning, particularly diffusion models, offer promising solutions. Diffusion models are known for their strong performance in generative tasks and are particularly well-suited for image restoration by iteratively refining degraded areas. These models can simulate the gradual addition and removal of noise, mimicking the natural restoration process, thereby enabling more precise reconstruction of damaged regions. Leveraging these advantages, this paper proposes a novel mural image restoration model architecture based on diffusion, aimed at addressing the specific challenges of restoring ancient murals. The proposed model combines a heterogeneous UNet structure with two key modules: pixel space augmentation block (PSAB) for enhancing spatial details and dual channel attention block (DCAB) for refining channel information. Experimental results show the model’s effectiveness in restoring large-scale damage and fine textures, outperforming existing methods in PSNR, SSIM, and LPIPS metrics.