用生成模型与变分法结合，精准识别画作裂纹，助力艺术修复。
Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings
- 将裂纹检测视为逆问题，分离画作与裂纹成分。
- 在真实画作数据上实现像素级裂纹定位，准确率高。
- 适合艺术保护、数字文物修复领域研究人员使用。
成像技术、深度学习与数值计算的进步，使艺术品的非侵入式精细分析成为可能，支持其记录与保护。特别是对数字化画作中裂纹的自动检测，对评估退化状况和指导修复至关重要，但因背景复杂且裂纹与笔触、发丝等艺术特征视觉相似，仍具挑战。本文提出一种混合方法，将裂纹检测建模为逆问题，将观测图像分解为无裂纹画作与裂纹成分。采用深度生成模型作为画作先验，裂纹结构则通过类Mumford-Shah的变分泛函与裂纹先验捕捉。联合优化得到画作中裂纹的像素级定位图。
原文摘要 · Abstract (English)
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.
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