Technology Category
Computer Vision: Diffusion Models for VisionCognitive Modeling & Cognitive Systems: Computational CreativityMachine Learning: Deep Generative Models & Autoencoders
Synthetic Craquelure Generation for Unsupervised Painting Restoration
Technology Category
Application Category
📝 Abstract
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 B\'ezier 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.
Problem
Research questions and friction points this paper is trying to address.
craquelure
painting restoration
unsupervised learning
cultural heritage
pixel-level annotation
Innovation
Methods, ideas, or system contributions that make the work stand out.
synthetic craquelure generation
annotation-free restoration
detector-guided refinement
Low-Rank Adaptation (LoRA)
anisotropic diffusion inpainting
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