Nighttime images suffer from low visibility due to insufficient illumination and glow effects caused by artificial light sources, which severely impair image information. Most existing low-light image enhancement algorithms are designed for underexposed images. Applying these methods directly to low-light images with glows often intensifies the glow regions and further degrades image visibility. Moreover, these algorithms typically require paired or unpaired datasets for network training. To address these issues, we propose a zero-shot enhancement method for low-light images with glows, leveraging a layer decomposition strategy. The proposed network comprises two main components: layer decomposition and illumination enhancement. The layer decomposition network integrates three sub-networks, including channel attention mechanism modules. Under the guidance of glow separation loss with an edge refinement term and self-constraint information retention loss, the input images is decomposed into three components: glow, reflection, and illuminance images. The illuminance map is subsequently processed via the illumination enhancement network to estimate enhancement parameters. The enhanced image is reconstructed by combining the reflection map and the enhanced illuminance map, following the Retinex theory. Experimental results demonstrate that the proposed method outperforms state-of-the-art unsupervised low-light image enhancement algorithms, achieving superior visualization effects, the best NIQE and PIQE indices, and a near-optimal MUSIQ index. The method not only effectively suppresses glows but also improves the visibility of dark regions, producing more natural enhanced images.