Fast Adaptive Low-Light Image Enhancement via Mixture of Experts
Abstract
Existing methods typically require training a separate model for each dataset, making them difficult to generalize across diverse illumination conditions. To address this limitation, we propose a novel low-light image enhancement method based on a Mixture of Experts (MoE) mechanism with fast adaptation. In our framework, the MoE gating network adaptively fuses the outputs of multiple experts to handle different lighting conditions, while only the expert and gating networks are fine-tuned when adapting to new datasets, significantly improving training efficiency and generalization. Each expert is designed as a multi-task module that jointly performs color correction and noise reduction, thereby enhancing both visual fidelity and robustness. Extensive quantitative and qualitative experiments demonstrate that the proposed method not only surpasses state-of-the-art approaches in noise reduction and color preservation, but also rapidly adapts to new illumination distributions with fast training across multiple benchmark datasets with significantly reduced fine-tuning cost and training time.