Towards Flexible and Efficient Diffusion Low Light Enhancer

• URL: http://arxiv.org/abs/2410.12346v1
• Date: Wed, 16 Oct 2024 08:07:18 GMT
• Title: Towards Flexible and Efficient Diffusion Low Light Enhancer
• Authors: Guanzhou Lan, Qianli Ma, Yuqi Yang, Zhigang Wang, Dong Wang, Yuan Yuan, Bin Zhao,
• Abstract summary: Diffusion-based Low-Light Image Enhancement (LLIE) has demonstrated significant success in improving the visibility of low-light images. We propose textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT), a step distillation framework specifically designed for LLIE.
• Score: 30.515393168075448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based Low-Light Image Enhancement (LLIE) has demonstrated significant success in improving the visibility of low-light images. However, the substantial computational burden introduced by the iterative sampling process remains a major concern. Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation. As a result, to achieve an efficient student model with performance comparable to that of existing multi-step teacher model, it is usually necessary to retrain a more capable teacher model. This approach introduces inflexibility, as it requires additional training to enhance the teacher's performance. To address these challenges, we propose \textbf{Re}flectance-aware \textbf{D}iffusion with \textbf{Di}stilled \textbf{T}rajectory (\textbf{ReDDiT}), a step distillation framework specifically designed for LLIE. ReDDiT trains a student model to replicate the teacher's trajectory in fewer steps while also possessing the ability to surpass the teacher's performance. Specifically, we first introduce a trajectory decoder from the teacher model to provide guidance. Subsequently, a reflectance-aware trajectory refinement module is incorporated into the distillation process to enable more deterministic guidance from the teacher model. Our framework achieves comparable performance to previous diffusion-based methods with redundant steps in just 2 steps while establishing new state-of-the-art (SOTA) results with 8 or 4 steps. Comprehensive experimental evaluations on 10 benchmark datasets validate the effectiveness of our method, consistently outperforming existing SOTA methods.

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