水下成像是海洋探索最常用的方法之一，越来越多的研究表明偏振是某些水下生物在低光照下拥有视觉的关键。提出了一种基于深度学习和偏振成像的多浑浊度水下图像恢复方法。通过拍摄清水和不同浑浊度的水下偏振图像获得多浑浊度水下偏振数据集。提出小尺寸神经网络，让网络更好地学习到不同浑浊度水下偏振信息到清晰水下图像的映射关系，并针对不同场景需求提出不同步长的滑动窗口叠加方法。结果表明，所提出的偏振方法能有效恢复水下图像，在不同浑浊度下恢复的峰值信噪比相比原图平均提高47.39%。该方法将深度学习与偏振成像技术相结合，能够在多浑浊度环境下恢复水下图像，克服了利用普通图像得到的水下图像恢复效果差的问题。

Underwater imaging is one of the most commonly used methods for ocean exploration, and a growing number of studies have shown that polarization is the key to the underwater creatures having vision in low illumination. In this paper, a multi-turbidity underwater image recovery method based on deep learning and polarization imaging is proposed. Multiple turbidity underwater polarization data sets are obtained by capturing images of clean water and underwater polarization images with different turbidity. A small size neural network is proposed to better learn the mapping relation between underwater polarization information under different turbidity and clear underwater images. A sliding window superposition method with different steps is proposed for different circumstances. The results show that the polarization method proposed in this paper can effectively recover the underwater image, and the peak signal to noise ratio recovered under different turbidity is 47.39% higher than that of the original image on average. The proposed method combining deep learning and polarization imaging technology can restore underwater images in multi-turbidity environment and overcome the problem of poor restoration effect of ordinary underwater images.