Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images.

水下图像处理在诸如海底地形扫描，海底通信电缆敷设，水下车辆，水下搜索和救援等各个领域中发挥了重要作用。然而，在获取水下图像的过程中存在许多困难。具体地，当光穿过水时，水体将选择性地吸收部分光，从而导致水下图像的颜色下降。同时，由于水中漂浮物的影响，光线有一定程度的散射，会带来严重的问题，例如细节模糊，与水下图像对比度低等。因此，利用图像处理技术恢复水下图像的真实外观具有很高的实用价值。为了解决上述问题，我们将色彩校正方法与去模糊网络相结合，以提高水下图像的质量。首先，针对水下图像样本数量不足和多样性的问题，提出了一种结合深度图像重建和水下图像生成的网络，基于样式转移方法对水下图像进行仿真。其次，针对色彩失真问题，提出了一种基于图像全局信息并结合水中光传播损耗定律的动态阈值色彩校正方法。最后，为了解决由散射引起的图像模糊的问题，并进一步提高整体图像的清晰度，通过多尺度递归卷积神经网络重建了色彩校正后的图像。实验结果表明，我们可以在较短的训练时间内获得更接近水下风格的图像。与几种最新的水下图像处理方法相比，该方法在多种水下场景中具有明显的优势。同时，我们可以恢复颜色信息，消除模糊并增强水下图像的细节。