In the underwater environment, the backscattering and attenuation of wavelength-dependent light degrade the quality of underwater vision. Low-quality underwater vision will reduce the accuracy of underwater robot visual navigation and pattern recognition. A novel semi-supervised deep convolutional neural network composed of a supervised learning branch and an unsupervised learning branch is proposed herein to improve underwater visual quality with poor visibility in real time. The network is constrained by a supervised loss function consisting of mean square, underwater index, and adversarial loss. The supervised branch serves as the baseline of the image enhancement algorithm to learn the basic feature information of the images and restore the original colors. The unsupervised learning branch, which makes the generated images more realistic and reduces reliance on the quality of the simulation model of synthetic data, applies underwater dark channel prior loss and total variation loss to learn the feature domain information of real images. Experiments show that the results of the proposed method show less color shift, lower fogging and blurring, and more pleasing high-quality vision. The enhanced images can extract more useful feature information, which is promising in the online visual navigation of underwater robots.

在水下环境中，与波长相关的光的反向散射和衰减会降低水下视觉的质量。低质量的水下视觉会降低水下机器人视觉导航和模式识别的准确性。本文提出了一种由监督学习分支和非监督学习分支组成的新型半监督深度卷积神经网络，以提高水下视觉质量，实时可见性差。该网络受监督损失函数的约束，监督损失函数包括均方，水下指数和对抗损失。受监督的分支用作图像增强算法的基线，以学习图像的基本特征信息并恢复原始颜色。无监督的学习分支，它使生成的图像更加逼真并减少了对合成数据模拟模型质量的依赖，应用水下暗通道先验损失和总变化损失来学习真实图像的特征域信息。实验表明，该方法的结果显示出较少的色偏，较低的起雾和模糊感，以及更令人愉悦的高质量视觉效果。增强的图像可以提取更多有用的特征信息，这在水下机器人的在线视觉导航中很有希望。更令人愉悦的高质量视觉 增强的图像可以提取更多有用的特征信息，这在水下机器人的在线视觉导航中很有希望。更令人愉悦的高质量视觉 增强的图像可以提取更多有用的特征信息，这在水下机器人的在线视觉导航中很有希望。