针对浑浊水体环境下图像对比度降低和色偏严重等问题，构建模拟真实浑浊水环境的水下图像数据集，提出了一种基于改进Shallow-UWnet网络模型的浑浊水体图像增强方法。首先，使用灰度空间算法对原始图像进行全局颜色校正，再利用改进的Shallow-UWnet网络模型，学习失真图像与正常图像的映射关系从而实现水下图像增强，然后使用限制对比度自适应直方图均衡化（contrast limited adaptive histogram equalization，CLAHE）方法提高图像对比度，从而得到最终的增强图像。实验结果表明，本方法在主客观评价指标和特征点匹配应用指标上优于其他5种参考方法，能有效校正不同浑浊水环境下图像的色偏，提升图像对比度和清晰度。本方法可以适用于水体较为浑浊的水下原位环境，为提高水下场景的视觉质量提供了有效的解决方案，在水下探测、水下救援、水下考古等领域中具有广泛的应用前景。

Aiming at the problems of image contrast reduction and serious color cast in turbid water, we constructed a dataset of underwater image for experimental turbid water, and proposed an image enhancement method based on improved Shallow-UWnet network. Firstly, we employed the al-gorithm of gray scale for global color correction to original images. And then we utilized the improved Shallow-UWnet network, which learned the mapping relationship between the dis-torted and the normal images, to achieve underwater image enhancement. Finally, we improve the contrast of images to obtain final results, by employing contrast limited adaptive histogram equalization (CLAHE). The experimental results show that our method is superior to other 5 ones not only in subjective and objective evaluation indexes but also in key points matching. And it is effectively in correcting the color cast in different turbid water and improving the con-trast and clarity. This method can be applied to underwater in-situ environment with turbidity, and is an available solution for improving underwater visualization. It has wide prospect in un-derwater detection, underwater salvation, underwater exploration and so on.

TP391

山东省自然科学基金重大基础研究项目(ZR2022ZD38)、青岛市科技计划重点研发专项（22-3-3-hygg-30-hy）