WCT-Net: joint restoration of tomb murals based on wavelet convolution and transformer self-attention collaborative network
Juanjuan Li, Meng Wu, Zhiyong Lu, Li Wang, Hang Zhao, Jian Li · npj Heritage Science · 2026
The digital restoration of tomb murals is critical for cultural heritage preservation. Existing research focuses on repairing internal lacunae and lacks an effective coordinated restoration solution for cross-scale compound deterioration involving coexisting peripheral information loss and internal degradation. To address this, we propose a restoration of tomb Murals based on Wavelet Convolution and Transformer Self-Attention Collaborative Network. The wavelet branch enhances local structure preservation by explicitly decoupling and reconstructing frequency-domain features, while the Transformer branch establishes long‑range semantic dependencies to ensure globally consistent structural extrapolation. An enhanced feature fusion unit collaboratively suppresses structural distortion and detail blur, achieving pixel-level high-fidelity restoration. Additionally, the multi-scale cross-layer feature aggregation module further strengthens the decoder’s reconstruction capability. Experiments on Tang Dynasty tomb mural fragments with missing peripheral information show our method improves structural fidelity (PSNR) by 3.5%, perceptual quality (LPIPS) by 15.1%, visual authenticity (FID) by 30.5%, demonstrating its effectiveness in restoring complex damage.