Ke Zhang

Scalable Image Restoration and Enhancement towards Diverse Downstream Applications

This paper proposes a scalable framework for image restoration and enhancement built upon the reinforcement learning (RL) paradigm, and confirms the model’s superior few-shot and zero-shot capabilities compared to existing methods, as well as its flexibility in addressing multiple competing objectives.