In recent years, Mamba has been playing an increasingly important role in the field of low-light image enhancement and has gradually surpassed traditional convolutional neural networks (CNNs) and Transformers. However, existing Mamba networks tend to focus exclusively on capturing global contextual semantic relations, overlooking the impact of local features on restoration under low-light conditions. Since CNNs and Transformers struggle to capture global degradation, while the state space model (SSM) within Mamba excels in long-sequence modeling, this paper introduces a novel global-to-local feature extraction approach upon integrating Mamba. We first propose the Global-to-Local Mamba Block to perform refined feature extraction in the low-frequency domain, and then complement high-frequency texture distortions via the high-frequency guided enhancement module using low-frequency features. Extensive experiments conducted on multiple datasets demonstrate that Global-to-Local Mamba achieves superior performance in low-light restoration and image enhancement.