Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution
#Bird-SR #Super-resolution #Diffusion models #Reward Feedback Learning #Image processing #Machine learning #arXiv
📌 Key Takeaways
- Researchers developed Bird-SR to improve image super-resolution on real-world data.
- The framework uses Reward Feedback Learning (ReFL) to optimize diffusion trajectories.
- It addresses the 'distribution shift' problem where models fail outside of synthetic training environments.
- Bird-SR combines synthetic pairs with unpaired real-world images for better structural fidelity.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Computer Vision, Technology
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📄 Original Source Content
arXiv:2602.07069v1 Announce Type: cross Abstract: Diffusion-based super-resolution can synthesize rich details, but models trained on synthetic paired data often fail on real-world LR images due to distribution shifts. We propose Bird-SR, a bidirectional reward-guided diffusion framework that formulates super-resolution as trajectory-level preference optimization via reward feedback learning (ReFL), jointly leveraging synthetic LR-HR pairs and real-world LR images. For structural fidelity easil