This paper proposes the Wavelet-Transform Region-Aware Low-Light Image Enhancement Network, WTR-Net. It addresses the challenge of significant differences in degradation between detail and flat regions under uneven illumination, which cannot be effectively optimized by a single enhancement strategy. The network uses wavelet transform to decompose the image into low-frequency and high-frequency sub-bands and constructs a high-frequency energy-guided regional segmentation mask. In detail regions, a Vision Transformer module is deployed to restore geometric structures, while in flat regions, the Dynamic Multi-Scale Feature Enhancement Network, DMSFE-Net employs smooth constraints to suppress noise accumulation. The network employs a joint optimization of high-frequency gradient-sensitive loss, low-frequency Huber smooth loss, and global fidelity loss to achieve collaborative optimization of multi-level degraded features. Experiments show that WTR-Net outperforms existing advanced methods on several benchmark datasets.