一种基于改进Shallow-UWnet的浑浊水体图像增强方法  

An image enhancement method for turbid water based on improved shallow-UWnet

作　　者：张文淇 张浩 牛志杰 白邵宙 田艳兵 吉爱国 ZHANG Wenqi;ZHANG Hao;NIU Zhijie;BAI Shaozhou;TIAN Yanbing;JI Aiguo(School of Information and Control Engineering,Qingdao University of Technology,Qingdao,Shandong 266520,China)

机构地区：[1]青岛理工大学信息与控制工程学院,山东青岛266520

出　　处：《光电子.激光》2025年第2期167-175,共9页Journal of Optoelectronics·Laser

基　　金：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)资助项目。

摘　　要：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,提升图像的对比度和清晰度。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 algorithm 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 distorted and the normal images,to achieve underwater image enhancement.Finally,we improved 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 5ones 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 contrast 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 underwater detection,underwater salvation,underwater exploration and so on.

关 键 词：图像处理 水下图像增强 浑浊水体 深度学习 改进Shallow-Uwnet网络模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]