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Author

Ke Zhang

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#artificial intelligence Preprint Mar 2025

Learning Where and What to Restore for Composite Image Restoration

A unified framework that jointly decides where to spend computation and what degradation cues should guide restoration, which achieves state-of-the-art performance for composite degradation restoration on CDD-11 and delivers the best results to date on the conventional 3-task and 5-task all-in-one benchmarks.

Jia-Chen Jiang, Tianyu Ding, Ke Zhang et al. · 5 citations
Aug 2026

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.

Juan Wang, Ke Zhang, Chun-Feng Yuan et al. · 0 citations

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