基于全局背景光强度估计和对比度调整的无监督水下图像增强  

Unsupervised underwater image enhancement with global background light intensity estimation and contrast adjustment

作　　者：霍冠英[1] 盛兴琳 施淑娴 HUO Guanying;SHENG Xinglin;SHI Shuxian(School of Information Science and Engineering,Hohai University,Changzhou 213200,China)

机构地区：[1]河海大学信息科学与工程学院,常州213200

出　　处：《东南大学学报(自然科学版)》2025年第1期297-305,共9页Journal of Southeast University:Natural Science Edition

基　　金：国家重点研发计划资助项目(2023YFB3907203);国家自然科学基金“叶企孙”科学基金资助项目(U2441254)。

摘　　要：提出了一种优化全局背景光强度估计和对比度的无监督水下图像增强方法。针对用高斯模糊估计全局背景光不准确的问题,提出一种全局背景光估计方法,利用包含上、下采样以及卷积的网络来精确估计全局背景光,并通过平滑约束来监督网络的学习;针对复杂成像环境、局部与全局处理不均衡等导致的色偏及对比度低的问题,提出一种对比度调整的综合方法,对网络施加对比度约束,并结合自动色阶和CLAHE方法,改善全局亮度和对比度并保持局部细节。基于自建的水下图像数据集和真实水下图像数据集进行水下图像增强实验。结果表明,该方法在视觉效果和定量指标上均表现突出。自建数据集图像增强后对比度高、细节清晰,在UCIQE、UIQM和综合指标上均表现最佳,分别达到0.54、0.55和44.76。An unsupervised underwater image enhancement method with global background light intensity esti-mation and contrast adjustment is proposed.Aiming at the inaccuracy of Gaussian blur in estimating global background light,a novel method is proposed.It utilizes a network incorporating upsampling,downsam-pling,and convolution to estimate the global background light,with a smoothness constraint to supervise the network’s learning.To tackle the issues of color cast and low contrast caused by complex imaging environ-ments and imbalanced local-global processing,a comprehensive approach for contrast adjustment is introduced to impose contrast constraints on the network.In addition,this approach integrates automatic levels and the contrast limited adaptive histogram equalization(CLAHE)method to enhance global brightness and contrast while preserving local details.Experiments were conducted based on a self-constructed underwater image data-set and a real underwater image dataset.The results indicate that this method achieves outstanding perfor-mance in visual effects and quantitative indicators.The images in the self-constructed dataset exhibit high con-trast and clear details after enhancement,outperforming others in terms of underwater color image quality evaluation(UCIQE),underwater image quality measure(UIQM),and a combined metric,achieving scores of 0.54,0.55,and 44.76,respectively.

关 键 词：水下图像增强 全局背景光强度估计 无监督学习 平滑损失 对比度调整 

分 类 号：TN911.73[电子电信—通信与信息系统]