Adaptation of the super resolution SOTA for Art Restoration in camera capture images
This provides an automated solution for art restoration that reduces time and expertise requirements, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of restoring deteriorated images of art pieces by adapting a state-of-the-art super-resolution diffusion model and fine-tuning it for art restoration, achieving robust performance across multiple degradation types without needing separate models for each.
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