Aiming at the problems of color distortion, nonuniform illumination, and low contrast caused by degradation of underwater images, an underwater image enhancement method (MSFF-GAN) based on generative adversarial network was proposed. A multiscale featured fusion generator is designed, which improves the ability to use different scale features of the model and ensures that the generated image retains more detailed information. The residual dense module is constructed to solve the problem of generator characteristics extracted slower. In the discriminator, to achieve the extraction of local image features, the output matrix is discriminating so that the generated image is closer to the real image. Compared with the existing underwater image enhancement methods qualitatively and quantitatively, the proposed method has better enhancement effect on EUVP and RUIE datasets. The proposed method is superior to the contrast method of three evaluation indexes: PSNR, SSIM, and UIQM.

中文翻译：

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基于生成对抗网络的水下图像增强方法

针对水下图像退化引起的色彩失真，照度不均匀，对比度低的问题，提出了一种基于生成对抗网络的水下图像增强方法（MSFF-GAN）。设计了多尺度特征融合生成器，该生成器提高了使用模型的不同尺度特征的能力，并确保生成的图像保留更多详细信息。构造残差密集模块以解决发电机特性提取速度较慢的问题。在鉴别器中，为了实现局部图像特征的提取，对输出矩阵进行鉴别，以使生成的图像更接近真实图像。与现有的水下图像增强方法相比，在定性和定量上，该方法对EUVP和RUIE数据集具有更好的增强效果。所提出的方法优于三个评估指标的对比方法：PSNR，SSIM和UIQM。