Low-light images often suffer from multiple intertwined degradation factors, leading to poor visual quality. Although recent low-light image enhancement (LLIE) models have achieved notable progress, they still face challenges in effectively disentangling these factors while balancing enhancement accuracy and computational efficiency. In this work, we propose an illumination-prior guided hybrid network that integrates the strengths of global feature extraction (Transformer and Mamba) and local feature extraction (CNN). To facilitate effective feature fusion, we exploit illumination priors directly derived from the input images through a brightness-aware dynamic gating mechanism. Along the main encoder – decoder pathway, a dynamic window strategy is employed to preserve multi-scale perception while keeping the model size compact. Furthermore, a wavelet-based refinement module is introduced to separately restore high-frequency textures and low-frequency illumination, further improving the final output quality. Extensive experiments on multiple LLIE benchmarks demonstrate that our method outperforms SOTA methods in enhancing performance with acceptable computational costs. Ablation studies further confirm the effectiveness of each key component.