壁画数字化修复旨在利用信息技术，填补壁画破损区域的缺失部分，从而复原其视觉上的完整性与艺术原貌。针对现有深度学习方法在古壁画图像修复过程中仅依赖自身先验信息，缺乏外部特征信息引导，导致修复结果易出现语义不一致与细节模糊问题，提出一种参考图像引导与风格增强的古壁画图像修复方法。首先，构建壁画图像修复主干网络，设计基于自适应跨尺度卷积的壁画特征编码模块，跨尺度提取的壁画内容特征，增强模型细节修复能力。其次，设计风格特征编码模块，通过其学习并提取参考壁画图像不同尺度的风格特征。接着，设计特征对齐融合模块，将壁画图像修复网络编码器提取的壁画内容特征与参考壁画图像风格特征进行对齐并融合，作为外部风格特征引导信息。然后，构建风格感知增强模块，对融合后的风格特征进行进一步细化，同时在修复网络解码部分设计动态特征引导层，引导模型解码修复过程，提升修复结果的语义一致性，输出修复后壁画图像。最后，在敦煌壁画数集上进行修复实验，并采用PSNR与SSIM客观评价指标进行量化分析，结果表明所提方法能够有效完成破损壁画图像的修复，且主客观评价优于比较方法。

Mural digital inpainting aims to employ information technology to fill the missing areas of damaged murals, thereby restoring their visual integrity and original artistic appearance.To address the limitations of existing deep learning methods for ancient mural image inpainting, which often solely rely on internal priors and lack external feature guidance, resulting in semantic inconsistency and blurred details, this paper proposes a reference image guided and style enhanced ancient mural image inpainting method. First a backbone network for mural image inpainting is constructed, along with a mural feature encoding module based on adaptive cross-scale convolution, which extracts mural content features across multiple scales to enhance the model’s ability in restoring fine-grained details. Next, a style feature encoding module is designed to learn and extract style features at different scales from reference mural images. Then, a feature alignment and fusion module is introduced to align and integrate the content features extracted by the encoder with the multi-scale style features from the reference image, enabling external style-guided information to participate in the inpainting process. Following this, a style-aware enhancement module is constructed to further refine the fused style features. Meanwhile, a dynamic feature guidance layer is designed within the decoder of the inpainting network to guide the restoration process, improving the semantic consistency of the final output mural. Finally, restoration experiments on the Dunhuang mural dataset were conducted, and quantitative analysis was performed using PSNR and SSIM as objective evaluation metrics. The results demonstrate that the proposed method can effectively restore damaged murals, achieving superior performance in both objective and subjective evaluations compared with existing methods.