SR-Ground: Image Quality Grounding for Super-Resolved Content
The research created SR-Ground, a large dataset of 63,000 super-resolved images with pixel-level annotations for six artifact types. This dataset showed that training image quality assessment models with grounding capabilities significantly improves performance on detecting and reducing subtle visual artifacts in super-resolution images.