FMAGAN:Fusing multiple attention and generative adversarial network to enhance underwater image

Autor: Yanhan Zhang, Long Hou, Shengwei Tian, Long Yu
Rok vydání: 2022
Předmět:
Zdroj: Journal of Intelligent & Fuzzy Systems. 42:2421-2433
ISSN: 1875-8967
1064-1246
Popis: 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.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje