4 papers
Low-Light Video Enhancement with An Effective Spatial-Temporal Decomposition Paradigm
Xiaogang Xu, Kun Zhou, Tao Hu +4
Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition s…
Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward
Xiaogang Xu, Ruihang Chu, Jian Wang +6
Reinforcement Learning (RL) has recently been incorporated into diffusion models, e.g., tasks such as text-to-image. However, directly applying existing RL methods to diffusion-bas…
Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement
Xiaogang Xu, Jian Wang, Yunfan Lu +5
Diffusion-based methods, leveraging pre-trained large models like Stable Diffusion via ControlNet, have achieved remarkable performance in several low-level vision tasks. However,…
Generative Distribution Distillation
Jiequan Cui, Beier Zhu, Qingshan Xu +6
In this paper, we formulate the knowledge distillation (KD) as a conditional generative problem and propose the \textit{Generative Distribution Distillation (GenDD)} framework. A n…