Affiliations: [a]
School of Software, XinJiang University, Urumqi, China
| [b]
Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China
| [c]
Network Center, XinJiang University, Urumqi, China
Correspondence:
[*]
Corresponding author. Long Yu, Network Center, XinJiang University, Urumqi, 830000, China. E-mail: [email protected].
Abstract: Underwater image enhancement has always been a hot spot in underwater vision research. However, due to complicated underwater environment, a lot of problems such as the color distortion and low brightness of underwater raw images are very likely to occur. In response to the above situation, we proposed a generative adversarial network that integrated multiple attention to enhance underwater images. In the generator, we introduced multi-layer dense connections and CSAM modules, of which the former could capture more detailed features and make use of previous features, while the latter could improve the utilization of the feature map. Meanwhile, we improved the enhancement effect of the generated image by combining VGG19 content loss function and SmoothL1 loss function. Finally, we verified the effectiveness of the proposed model through qualitative and quantitative experiments, and compared the results with the performance of several latest models. The results show that the methods proposed in this paper are superior to the existing methods.
Keywords: Deep learning, attentional
mechanism, underwater image, image enhancement.
