<p>Low-light image enhancement (LLIE) aims to improve the brightness of images under low illumination while preserving details and structural information. Although methods based on Retinex theory and Transformer architectures have made significant progress, their dependence on large-scale training data and high computational costs limit practical applications. To address these challenges, researchers have proposed enhancement algorithms based on state-space models (SSM), which significantly reduce computational complexity while maintaining global modeling capabilities. However, existing methods still face issues such as color distortion and noise interference. To tackle these problems, we propose an innovative multi-stream SSM-based low-light image enhancement algorithm, MMamba-LLIE. This algorithm integrates three key modules: (1) a Color Correction Module (CCM) to effectively mitigate color distortion caused by Retinex theory; (2) a Multi-scale Feature Extraction Module (DFM) to capture both global and local structural information; and (3) a Noise Removal Module (UnoisyM) to suppress low-light noise. Experimental results demonstrate that MMamba-LLIE achieves significant improvements on the LOLv2-real dataset, with a 0.302 dB increase in PSNR, a 0.004 increase in SSIM, and a 0.65 reduction in RMSE. On the unparameterized DICM dataset, NIQE and PI are reduced by 0.026 and 0.051, respectively. Extensive experiments validate the superiority of the proposed method in both performance metrics and visual quality, providing a promising solution for low-light image enhancement. For details, please visit: <a href="https://github.com/lsaixuexi/MMamba-LLIE">https://github.com/lsaixuexi/MMamba-LLIE</a></p>