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：保护文化遗产至关重要。在艺术修复领域，开发能够有效修复艺术品退化图像的计算机视觉模型曾面临挑战，但如今我们已拥有成熟的计算机视觉技术。传统修复方法往往耗时且需要专业知识。本研究旨在设计基于计算机视觉模型的自动化解决方案，以增强和重建受损艺术品，在保留其原始特征与艺术痕迹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、褪色及其他常见损伤形式。我们基于扩散模型（DM）适配当前图像超分辨率的最新技术，并通过微调实现艺术品图像修复。实验结果表明，微调单一超分辨率模型可替代针对不同退化类型分别微调多个模型。我们采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