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
张文淇 1张浩 1牛志杰 1白邵宙 1田艳兵 1吉爱国1
作者信息
- 1. 青岛理工大学信息与控制工程学院,山东青岛 266520
- 折叠
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.
Abstract
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 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 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Key words
image processing/underwater image enhancement/turbid water/deep learning/improved Shallow-UWnet network model引用本文复制引用
出版年
2025
NETL
NSTL
万方数据