Digital camera sensors capture natural images in low-light conditions, resulting in poor imaging results. Existing low-light image enhancement (LLE) often yields unnatural results due to over-enhancement, artifacts, severe noise, etc. Prior studies either perform visual dual-pathway mechanisms or Retinex prior optimization for image enhancement. However, image enhancement based on the former generates artifacts because it directly stretches contrast in the structural layer with mixed high- and low-frequency information. The latter results in over-enhancement due to adding empirical prior items to the objective function. Thus, a unified three-pathway framework is proposed to address the aforementioned deficiencies for LLE. Specially, the proposed framework is composed of detail pathway, reflection pathway, and illuminance pathway. First, three information processing pathways can be obtained through different image decomposition strategies. Second, an indirect noise suppression strategy is developed in the computational flow of detail pathway and reflection pathway to address noise amplification problem of image enhancement. Third, the naturalness preservation enhancement task is conducted in the reflection pathway and illuminance pathway. Finally, the outputs of different pathways are weighted and fused to enhance low-light image. Moreover, qualitative and quantitative experimental results on two test datasets show that the proposed framework outperforms state-of-the-art methods.