摘要
针对现有水下图像增强算法不能感知降质,易丢失细节,无法有效纠正色偏等问题,提出了降质感知的小波变换水下图像增强网络模型.该模型主要包含对比学习的降质表征提取网络和多级小波变换的水下图像增强网络.首先,降质表征提取网络利用编码器和对比学习的方法,从每张水下图像中提取特有的降质表征;随后,以多级小波变换增强算法为指导思想,构建三级小波变换模块,旨在从频率域上实施多尺度的细节和颜色增强;最后,构建基于三级小波变换模块的多级小波变换增强网络,并将提取的降质表征引入到该网络中,以便在感知降质信息后,更好地实施多级小波变换增强.实验结果表明,本算法较已有算法具有更强的颜色校正,细节增强能力,增强结果在结构相似性指标上提升16％,峰值信噪比指标上提升9％,水下图像质量指标上提升14％,能用于水下图像增强任务.
Abstract
To solve the problems of poor degradation awareness,easy detail loss,and ineffective color cast correction caused by existing underwater image enhancement algorithms,this study proposes a degradation-aware underwater image enhancement network based on wavelet transform.This model mainly contains a degradation representation extraction network based on contrastive learning and an underwater image enhancement network based on multiple-level wavelet transform.Firstly,the degradation representation extraction network uses an encoder and contrastive learning method to extract unique degradation representations from each underwater image.Secondly,a three-level wavelet transform module is built under the principle of multi-level wavelet transform enhancement algorithm,aiming to conduct multi-scale detail and color enhancement in the frequency domain.Lastly,a multiple-level wavelet transform enhancement network is built with three-level wavelet transform modules,and the extracted degradation representations are introduced into this network for better implementing multiple-level wavelet transform enhancement with perceived degradation information.Experimental results show that the proposed algorithm outperforms existing algorithms in color correction and detail enhancement in terms of sharply enhanced results,i.e.structural similarity is improved by 16％,peak signal-to-noise ratio is improved by 9％,and underwater image quality is improved by 14％,making it suitable for underwater image enhancement tasks.
基金项目
宁夏科技创新领军人才计划(2022GKLRLX03)
国家自然科学基金(12202219)
维普
万方数据