Underwater vision technology is of great significance in marine investigation. However, the complex underwater environment leads to some problems, such as color deviation and high noise. Therefore, underwater image enhancement has been a focus of the research community. In this paper, a new underwater image enhancement method is proposed based on a generative adversarial network (GAN). We embedded the channel attention mechanism into U-Net to improve the feature utilization performance of the network and used the generator to estimate the parameters of the simplified underwater physical model. At the same time, the adversarial loss, the perceptual loss, and the global loss were fused to train the model. The effectiveness of the proposed method was verified by using four image evaluation metrics on two publicly available underwater image datasets. In addition, we compared the proposed method with some advanced underwater image enhancement algorithms under the same experimental conditions. The experimental results showed that the proposed method demonstrated superiority in terms of image color correction and image noise suppression. In addition, the proposed method was competitive in real-time processing speed.