RelicFormer: Digital Rebirth of Ancient Murals via Semantic Prior Distillation

The restoration of painted cultural relics is crucial for cultural heritage conservation, but existing deep learning methods are mostly oriented towards general images, making it difficult to meet their special requirements of faithfully restoring materials, colors and cultural semantics. To this end, we have constructed the first benchmark for painted cultural relic restoration, and proposed the RelicFormer method, which guides restoration by distilling the global semantic prior of the pre-trained Vision Transformer (ViT). On the test set containing 889 murals, this method significantly outperforms existing technologies in metrics such as PSNR (29.87dB) and SSIM (0.9287). In particular, it exhibits stronger structural fidelity and semantic coherence in moderately and severely damaged areas, providing an effective solution and evaluation benchmark for high-fidelity digital restoration of cultural relics.