本文提出了一种线框引导与Transformer增强U-Net（LDG-TEUN）的敦煌壁画图像修复方法。首先，在编码端嵌入融合轴向注意力与二维位置编码的交叉注意力模块，通过有效建模全局结构与长距离依赖关系，缓解大范围缺损造成的结构信息缺失问题。其次，设计双域部分卷积（DPConv）单元，联合对空间域与频率域特征进行建模，以此增强模型对复杂纹理和边缘细节的刻画能力，解决细节还原困难的问题。最后，构建复合损失函数，从结构一致性、纹理保真度与色彩分布合理性三个维度协同约束训练，进而提升整体修复效果，尤其在色彩复原方面更有助于贴近壁画的历史原貌。实验结果表明本方法在结构连贯性和色彩复原方面均取得了更优性能，验证了其在敦煌壁画数字化修复中的有效性和实用价值。

This paper introduces a line-drawing-guided Transformer-enhanced U-Net (LDG-TEUN) for the digital restoration of Dunhuang murals. A cross-attention module that integrates axial attention with two-dimensional positional encoding is embedded in the encoder to capture global structures and long-range dependencies, thereby alleviating the structural loss caused by large-scale damage. A dual-domain partial convolution (DPConv) unit is then designed to jointly model spatial- and frequency-domain features, enhancing the reconstruction of complex textures and fine edges while addressing challenges in detail recovery. Finally, a composite loss function is formulated to enforce structural consistency, texture fidelity, and color distribution simultaneously, which improves overall restoration quality and, in particular, enables more authentic color reconstruction. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both structural coherence and color restoration, confirming its effectiveness and practical value for the digital conservation of Dunhuang murals.