Recent advances in imaging technologies, deep learning and numerical performance have enabled non-invasive detailed analysis of artworks, supporting their documentation and conservation. In particular, automated detection of craquelure in digitized paintings is crucial for assessing degradation and guiding restoration, yet remains challenging due to the possibly complex scenery and the visual similarity between cracks and crack-like artistic features such as brush strokes or hair. We propose a hybrid approach that models crack detection as an inverse problem, decomposing an observed image into a crack-free painting and a crack component. A deep generative model is employed as powerful prior for the underlying artwork, while crack structures are captured using a Mumford--Shah-type variational functional together with a crack prior. Joint optimization yields a pixel-level map of crack localizations in the painting.

翻译：近年来，成像技术、深度学习与计算性能的进步使得对艺术品进行非侵入式精细分析成为可能，为其建档与保护工作提供了支持。特别是在数字化绘画中，龟裂的自动检测对于评估劣化程度和指导修复工作至关重要，但由于画面场景可能复杂，且裂纹与笔触或发丝等类裂纹艺术特征在视觉上相似，该任务仍具挑战性。我们提出一种混合方法，将裂纹检测建模为逆问题，将观测图像分解为无裂纹绘画与裂纹分量两部分。该方法采用深度生成模型作为底层艺术品的强先验，同时结合Mumford--Shah型变分泛函与裂纹先验来捕捉裂纹结构。通过联合优化，最终生成绘画中裂纹定位的像素级图谱。