Abstract: Based on the characteristics of color cast and blur in underwater images, we proposed a StarGAN (Star generative adversarial networks) based on CBAM (Convolutional block attention module) improvement for the underwater multi turbidity image enhancement to address the problem of significant differences in underwater images with different turbidity levels. First, we collected two sets of underwater turbidity image datasets from laboratory and aquaculture platform environments by using an underwater camera. Secondly, we optimized StarGAN by introducing a CBAM consisting of a channel attention module and a spatial attention module in series after each ResidualBlock module. Finally, we conducted ablation experiments and compared them with other methods by using UIQM (Underwater color image quality measurement), UCIQE (Underwater color image quality evaluation)and Image entropy as image quality evaluation indicators. The results show that UIQM reached 1.18, UCIQE reached 30.13 and Image entropy reached 12.83 of the enhanced laboratory dataset. UIQM reached 0.52, UCIQE reached 10.35 and Image entropy reached 9.94 of the enhanced aquaculture platform dataset. The experimental results indicate that in ablation experiments and compared with the other methods, this method has a good effect on enhancing multi turbidity images, with the highest scores.