Cultural heritage preservation increasingly demands non-invasive digital methods for painting restoration, yet identifying and restoring fine craquelure patterns from complex brushstrokes remains challenging due to scarce pixel-level annotations. We propose a fully annotation-free framework driven by a domain-specific synthetic craquelure generator, which simulates realistic branching and tapered fissure geometry using Bézier trajectories. Our approach couples a classical morphological detector with a learning-based refinement module: a SegFormer backbone adapted via Low-Rank Adaptation (LoRA). Uniquely, we employ a detector-guided strategy, injecting the morphological map as an input spatial prior, while a masked hybrid loss and logit adjustment constrain the training to focus specifically on refining candidate crack regions. The refined masks subsequently guide an Anisotropic Diffusion inpainting stage to reconstruct missing content. Experimental results demonstrate that our pipeline significantly outperforms state-of-the-art photographic restoration models in zero-shot settings, while faithfully preserving the original paint brushwork.

翻译：文化遗产保护日益需要非侵入式的数字绘画修复方法，然而由于像素级标注稀缺，从复杂笔触中识别并修复精细的龟裂纹图案仍具挑战。我们提出一种完全无需标注的框架，其核心为领域专用的合成裂纹生成器，该生成器利用贝塞尔轨迹模拟具有真实感的分支状及渐细裂缝几何形态。我们的方法将经典形态学检测器与基于学习的精细化模块相结合：该模块采用通过低秩自适应（LoRA）调整的SegFormer主干网络。我们创新性地采用检测器引导策略，将形态学图谱作为输入空间先验信息注入网络，同时通过掩码混合损失与逻辑值调整约束训练过程，使其专注于精细化候选裂纹区域。精细化后的掩码进而引导各向异性扩散修复阶段以重建缺失内容。实验结果表明，在零样本设置下，我们的流程显著优于当前最先进的摄影修复模型，同时能忠实保留原始绘画笔触。