Adaptation of the Super Resolution SOTA for Art Restoration in Camera Capture Images

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-the-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. We adapt the current state-of-the-art for image super-resolution based on a diffusion model and fine-tune it for art restoration, handling a diverse range of deterioration types including noise, blur, scratches, and fading.

In 2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), pp. 158–163