Underwater image enhancement is receiving increasing attention due to many of the vision research being applied to underwater scenes. To eliminate the impact of complex underwa ter scenes on imaging, underwater image enhancement algorithm has become an effective solution. However, underwater image enhancement models face a challenge of lighten ing the model while improving generalizability. Here, DGC-UWnet is proposed to go for both lightweight and enhancement effect. The proposed model is designed by using depth wise convolution, group convolution and channel shuffle (DGC). Ablation experiment shows that compared with standard convolution, DGC decreases model parameters and computational complexity, and improves the generalizability of the model. Qualitative and quantitative comparative experimental results show that comprehensive performances of the model can catch up with or even surpass state-of-the-art (SOTA) algorithms in terms of processing speed, subjective visual perception and objective evaluation metrics. In addi tion, application test results prove that DGC-UWnet can be used as the pre-processing for underwater applications of other visual algorithms such as improving performance of YOLOv5l.