Enhancement of underwater images on improved StarGAN by mixed attention module

Based on the characteristics of color cast and blur in underwater images, we proposed a StarGAN based on CBAM improvement for 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, UCIQE and Image entropy as image quality evaluation indicators. The results indicate that UIQM reached 1.18, Image entropy reached 12.83, and UCIQE reached 30.13 of the enhanced laboratory dataset. UIQM reached 0.52, Image entropy reached 9.94, and UCIQE reached 10.35 of the enhanced aquaculture platform dataset. The experimental results show that in ablation experiments and compared with other methods, this method has a good effect on enhancing multi turbidity images, with the highest scores.