Dunhuang murals suffer from fading, breakage, surface brittleness and extensive peeling affected by prolonged environmental erosion. Image inpainting techniques are widely used in the field of digital mural inpainting. Generally speaking, for mural inpainting tasks with large area damage, it is challenging for any image inpainting method. In this paper, we design a multi-stage progressive reasoning network (MPR-Net) containing global to local receptive fields for murals inpainting. This network is capable of recursively inferring the damage boundary and progressively tightening the regional texture constraints. Moreover, to adaptively fuse plentiful information at various scales of murals, a multi-scale feature aggregation module (MFA) is designed to empower the capability to select the significant features. The execution of the model is similar to the process of a mural restorer (i.e., inpainting the structure of the damaged mural globally first and then adding the local texture details further). Our method has been evaluated through both qualitative and quantitative experiments, and the results demonstrate that it outperforms state-of-the-art image inpainting methods.

翻译：敦煌壁画长期受环境侵蚀影响，存在褪色、断裂、表面脆化及大面积剥落等问题。图像修复技术广泛应用于数字壁画修复领域。一般而言，对于大范围破损的壁画修复任务，任何图像修复方法都面临巨大挑战。本文设计了一种包含全局到局部感受野的多阶段渐进式推理网络（MPR-Net），该网络能够递归推断破损边界，并逐步收紧区域纹理约束。此外，为自适应融合壁画多尺度丰富信息，我们设计了多尺度特征聚合模块（MFA），增强对重要特征的选择能力。该模型的执行过程类似于壁画修复师的工作流程（即先全局修复破损壁画的结构，再进一步补充局部纹理细节）。通过定性与定量实验评估，结果表明我们的方法优于当前最优的图像修复方法。