The generative adversarial network is widely used in image generation, and the generation of images with different styles is applied to underwater image enhancement. The existing underwater image generative adversarial network does not realize color correction when processing underwater images Therefore, we propose an improved generative adversarial network for image color restoration. Firstly, the loss function in the network is improved to train the dataset. Then the improved network is used to detect the underwater image. After network testing, the underwater image is more satisfactory than the traditional image. Numerical results show that this method has a good color restoration and sharpening effects.

中文翻译：

---

使用改进的生成对抗网络增强水下图像

生成对抗网络广泛应用于图像生成，将不同风格图像的生成应用于水下图像增强。现有的水下图像生成对抗网络在处理水下图像时没有实现颜色校正，因此，我们提出了一种改进的生成对抗网络用于图像颜色恢复。首先，改进网络中的损失函数来训练数据集。然后使用改进的网络来检测水下图像。经过网络测试，水下图像比传统图像更令人满意。数值结果表明，该方法具有良好的色彩还原和锐化效果。