F2IDiff: Super-resolution with Feature-to-Image Diffusion
Analysis
Key Takeaways
- •Proposes F2IDiff, a novel SISR approach using DINOv2 features for improved conditioning.
- •Addresses the limitations of using text-based features in SISR for high-fidelity images.
- •Aims to reduce hallucinations and improve the quality of super-resolved images in real-world scenarios, especially for smartphone photography.
“The paper introduces an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM).”