20 citations · 20 across the 5 of their papers we have counts for
6 papers
Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration
Xu Zhang, Jun Wan, Lefei Zhang +1
Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and harmful outputs go unnoticed u…
SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration
Jie Shao, Shengkai Hu, Xu Zhang +4
This paper studies agentic image restoration, in which multimodal agents coordinate specialized restoration tools to recover images affected by complex degradations. Existing resto…
SpikeRestormer: Towards Energy-Efficient All-in-One Image Restoration via Unified Event Reasoning
Shengkai Hu, Jie Shao, Jiaqi Ma +5
ANN-based All-in-One image restoration (AiOIR) unifies diverse degradation handling but incurs high computational costs, limiting its real-time deployment. While Spiking Neural Net…
ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration
Xu Zhang, Huan Zhang, Guoli Wang +2
All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on…
EvoIR: Towards All-in-One Image Restoration via Evolutionary Frequency Modulation
Jiaqi Ma, Shengkai Hu, Xu Zhang +4
All-in-One Image Restoration (AiOIR) tasks often involve diverse degradation that require robust and versatile strategies. However, most existing approaches typically lack explicit…
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
Xu Zhang, Huan Zhang, Guoli Wang +3
Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degrad…