Most existing low-light image enhancement (LLIE) methods mainly focus on adjusting overall brightness of images. However, low-light images typically suffer from a low signal-to-noise ratio (SNR), making them highly prone to noise and visual artifacts after enhancement. These artifacts severely degrade the visual quality and naturalness of enhanced results. To address this problem, we propose a novel SNR-Aware Mamba-Transformer (SMT) network for high-quality low-light enhancement. Specifically, we introduce an SNR-aware Mamba module for effective global noise modeling, guided by pixel-level SNR cues. Additionally, we design an eight-directional mamba scanning mechanism to enable multi-perspective contextual feature aggregation, which further improves noise robustness and spatial coherence. To restore fine-grained details, we incorporate a multi-scale attention aggregation module that efficiently fuses features at different levels. Extensive experiments on eight public benchmark datasets demonstrate that our SMT not only achieves visually superior results but also outperforms or matches state-of-the-art methods on multiple quantitative image quality metrics.