Abstract: Underwater imaging technology faces challenges such as reduced contrast and color shifts caused by light absorption and scattering in marine environments， Which is particularly evident in image dependent applications in marine and underwater environments. To overcome these challenges, we introduce a framework called a lightweight modular underwater network (LMUW-Net). This framework is designed to improve the visual quality and color fidelity of underwater images without significantly increasing network complexity. This framework first enhances the structural features of underwater images through basic feature extraction. It then employs advanced three-channel colors(red, green, and blue) enhancement and global sparse feature enhancement to effectively extract global image information, enhancing contrast and brightness in degraded underwater images. LMUW-Net significantly reduces computational complexity and memory usage through network structure optimization and parameter-sharing mechanisms, enabling real-time processing while significantly improving visual quality and color fidelity. Extensive experimental results across multiple publicly available underwater image datasets have shown that LMUW-Net, with only 9 000 trainable parameters, improves image quality indicators such as peak signal-to-noise ratio by 5% and structural similarity index by 3% compared to existing methods. This highlights its significant advantages in enhancing underwater image visual quality and computational efficiency. Overall, LMUW-Net provides a robust solution for underwater image processing, especially suitable for applications requiring real-time processing by improving image clarity.