A Reference Image-Guided and Style-Enhanced Restoration Method for Ancient Mural Images

To address the issues of semantic inconsistency and detail blurring in the restoration of ancient mural images using existing deep learning methods, which often solely rely on their own internal prior information and lack external feature guidance, this paper proposes a reference image-guided and style-enhanced restoration method for ancient mural images. First, a backbone network for mural image restoration was constructed, along with a mural feature encoding module based on adaptive cross-scale convolution, which extracts mural content features across scales, thereby enhancing the model’s ability to restore fine details. Second, a style feature encoding module was designed to learn and extract multi-scale style features from reference mural images. Third, a feature alignment and fusion module was introduced to align and fuse the mural content features extracted by the encoder of the mural image restoration network with the style features of the reference mural image, serving as external style feature guidance. Fourth, a style perception enhancement module was constructed to further refine the fused style features. Meanwhile, a dynamic feature guidance layer was designed within the decoding part of the restoration network to guide the restoration process, improving the semantic consistency of the final restored mural image. Finally, restoration experiments were conducted on the Dunhuang mural dataset. Quantitative analysis was performed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) as objective evaluation metrics. The results indicate that the proposed method can effectively restore the damaged mural images, achieving superior performance in both objective and subjective evaluations compared with competing methods.