Preserving cultural heritage is of paramount importance. In the domain of art restoration, developing a computer vision model capable of effectively restoring deteriorated images of art pieces was difficult, but now we have a good computer vision state-of-art. Traditional restoration methods are often time-consuming and require extensive expertise. The aim of this work is to design an automated solution based on computer vision models that can enhance and reconstruct degraded artworks, improving their visual quality while preserving their original characteristics and artifacts. The model should handle a diverse range of deterioration types, including but not limited to noise, blur, scratches, fading, and other common forms of degradation. We adapt the current state-of-art for the image super-resolution based on the Diffusion Model (DM) and fine-tune it for Image art restoration. Our results show that instead of fine-tunning multiple different models for different kinds of degradation, fine-tuning one super-resolution. We train it on multiple datasets to make it robust. code link: https://github.com/Naagar/art_restoration_DM

翻译：文化遗产保护至关重要。在艺术修复领域，开发能够有效修复艺术品图像退化的计算机视觉模型曾面临困难，但如今已具备成熟的计算机视觉技术。传统修复方法通常耗时且依赖专业知识。本研究旨在设计基于计算机视觉模型的自动化解决方案，以增强与重建受损艺术品图像，在保留原始特征与艺术细节的同时提升视觉质量。该模型需处理多种退化类型，包括但不限于噪声、模糊、划痕、褪色及其他常见劣化形式。我们基于扩散模型（Diffusion Model, DM）的图像超分辨率当前最优方法进行适配，并针对艺术品图像修复进行微调。实验结果表明，相比为不同退化类型分别微调多个模型，仅对单一超分辨率模型进行微调即可取得良好效果。我们使用多数据集训练以增强模型鲁棒性。代码链接：https://github.com/Naagar/art_restoration_DM