<p>Images captured in low-light environments frequently suffer from varying degrees of degradation, including insufficient illumination, noise, and color distortion. Although traditional end-to-end approaches and Retinex-based decomposition methods have achieved some success in mitigating these problems, they either lack sufficient interpretability or struggle to overcome the ill-posed Retinex decomposition. In this paper, we propose a novel framework that leverages a transfer function pipeline, integrating color space conversion and wavelet transforms to decompose the low-light image enhancement task into three distinct subtasks: illumination enhancement, noise reduction, and color restoration. Building on this approach, we introduce SAWTNet (Structure Awareness Wavelet Transformer Network), a model comprising three specialized subnetworks, each tailored to tackle one of the decoupled subtasks. Moreover, we propose a new loss function designed to enhance the estimation of the transfer function and improve adaptability to the demands of low-light image enhancement. Extensive experimental results across multiple datasets demonstrate the superiority of our proposed SAWTNet over other state-of-the-art methods, both in quantitative metrics and qualitative evaluations.</p>