Sinha, Saptarshi NeilSaptarshi NeilSinhaKühn, JuliusJuliusKühnKoppe, JohannesJohannesKoppeGraf, HolgerHolgerGrafWeinmann, MichaelMichaelWeinmann2025-03-192025-03-192024https://publica.fraunhofer.de/handle/publica/48569010.1109/CW64301.2024.00062Deep learning presents promising solutions for the restoration and preservation of visual arts, including old color photographs or paintings, which are prone to degradation over time, enabling the vibrant imagery to be effectively revived and maintained. In this paper, we propose a methodology for restoring visual arts based on deep learning techniques purely trained on synthetic data, thereby involving the generation of a dataset that incorporates respective defects, the training of a respective defect segmentation model, and the inpainting using predicted segmentation maps. Through qualitative and quantitative analysis, we demonstrate the potential of our approach in addressing the scarcity of ground truth data and effectively restoring old visual arts by synthetic training on specific defects observed in historical artworks.enBranche: Cultural and Creative EconomyResearch Line: Computer vision (CV)Research Line: Machine learning (ML)LTA: Generation, capture, processing, and output of images and 3D modelsSynthetic defect generationArtwork restorationSegmentationDeep learningconference paper