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Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward

arXiv — cs.CV•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
A new study explores the integration of Reinforcement Learning (RL) into diffusion-based image restoration models, highlighting the unique challenges of restoration compared to generation. This research is significant as it aims to enhance the fidelity of restored images, which is crucial for various applications in visual media. By adapting RL techniques specifically for restoration tasks, the study could lead to improved outcomes in image quality, making it a valuable contribution to the field.
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