DiffuMural：基于多尺度扩散的敦煌壁画修复
DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion
AI总结：
针对古代壁画缺损区域大、训练样本稀缺的修复挑战，提出DiffuMural方法，结合多尺度收敛、协同扩散机制、ControlNet及循环一致性损失，利用23幅敦煌壁画训练数据，在多指标上优于现有SOTA方法。
摘要：
大规模预训练扩散模型在条件图像生成领域取得了优异成果。然而，作为该领域重要下游任务的古代壁画修复，因其缺损区域大、训练样本稀缺，对基于扩散模型的修复方法构成重大挑战。条件修复任务更关注修复部分在整体风格和接缝细节上是否符合壁画修复的审美标准，而当前研究缺乏此类评估启发式图像补全的指标。因此，我们提出DiffuMural，一种结合多尺度收敛和协同扩散机制、ControlNet及循环一致性损失的方法，以优化生成图像与条件控制的匹配度。DiffuMural利用23幅具有一致视觉美学的大规模敦煌壁画训练数据，在修复复杂细节、实现整体外观连贯以及应对缺乏事实依据的不完整壁画所带来的独特挑战方面表现出卓越能力。我们的评估框架纳入了四个关键指标来定量评估不完整壁画：事实准确性、纹理细节、上下文语义和整体视觉连贯性。此外，我们整合了人文价值评估，以确保修复后的壁画保留其文化和艺术意义。大量实验验证，我们的方法在定性和定量指标上均优于现有最先进（SOTA）方法。
英文摘要：
Large-scale pre-trained diffusion models have produced excellent results in the field of conditional image generation. However, restoration of ancient murals, as an important downstream task in this field, poses significant challenges to diffusion model-based restoration methods due to its large defective area and scarce training samples. Conditional restoration tasks are more concerned with whether the restored part meets the aesthetic standards of mural restoration in terms of overall style and seam detail, and such metrics for evaluating heuristic image complements are lacking in current research. We therefore propose DiffuMural, a combined Multi-scale convergence and Collaborative Diffusion mechanism with ControlNet and cyclic consistency loss to optimise the matching between the generated images and the conditional control. DiffuMural demonstrates outstanding capabilities in mural restoration, leveraging training data from 23 large-scale Dunhuang murals that exhibit consistent visual aesthetics. The model excels in restoring intricate details, achieving a coherent overall appearance, and addressing the unique challenges posed by incomplete murals lacking factual grounding. Our evaluation framework incorporates four key metrics to quantitatively assess incomplete murals: factual accuracy, textural detail, contextual semantics, and holistic visual coherence. Furthermore, we integrate humanistic value assessments to ensure the restored murals retain their cultural and artistic significance. Extensive experiments validate that our method outperforms state-of-the-art (SOTA) approaches in both qualitative and quantitative metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
