Abstract
Low-light enhancement has wide applications in autonomous driving, 3D reconstruction, remote sensing, surveillance, and so on, which can significantly improve information utilization. However, most existing methods lack generalization and are limited to specific tasks such as image recovery. To address these issues, we propose \textbf{Gated-Mechanism Mixture-of-Experts (GM-MoE)}, the first framework to introduce a mixture-of-experts network for low-light image enhancement. GM-MoE comprises a dynamic gated weight conditioning network and three sub-expert networks, each specializing in a distinct enhancement task. Combining a self-designed gated mechanism that dynamically adjusts the weights of the sub-expert networks for different data domains. Additionally, we integrate local and global feature fusion within sub-expert networks to enhance image quality by capturing multi-scale features. Experimental results demonstrate that the GM-MoE achieves superior generalization with respect to 25 compared approaches, reaching state-of-the-art performance on PSNR on 5 benchmarks and SSIM on 4 benchmarks, respectively.
Abstract (translated)
低光增强技术在自动驾驶、三维重建、遥感、监控等领域有广泛的应用，可以显著提高信息利用效率。然而，现有的大多数方法缺乏泛化能力，并且局限于特定任务如图像恢复。为解决这些问题，我们提出了**门控机制专家混合网络（GM-MoE）**，这是首个引入专家混合网络以进行低光图像增强的框架。GM-MoE包含一个动态门控权重调节网络和三个子专家网络，每个子专家网络专注于不同的增强任务。通过结合自设计的门控机制来动态调整针对不同数据域的子专家网络权重。此外，我们还在子专家网络中集成了局部与全局特征融合功能，以捕捉多尺度特征从而提升图像质量。实验结果表明，GM-MoE在25种比较方法中表现出色，并且在五个基准测试上达到了PSNR指标上的最新性能，在四个基准测试上达到SSIM指标的领先表现。
URL
https://arxiv.org/abs/2503.07417
