机械科学与技术2025，Vol.44Issue(3)：505-512,8.DOI:10.13433/j.cnki.1003-8728.20230208
多尺度注意力网络的水下图像增强算法
Underwater Image Enhancement Algorithm with Multi-scale Attention Networks
摘要
Abstract
Aiming at the problems of serious color cast and blurring of images in underwater environment,this paper proposes a novel generative adversarial network algorithm.U-Net is used as the basic model of the generative network and improved.Firstly,the attention mechanism is introduced into the network,and a multi-scale feature extraction module is designed to extract features at different levels.Secondly,the robustness of the model is improved by preprocessing the input white balance image.In order to solve the problem of uneven restoration of image details caused by a single loss,L1 loss and content loss are combined in the traditional adversarial loss function.The experimental results show that this method has a good effect on color recovery and sharpness improvement of underwater images,where the average value of structural similarity,peak signal-to-noise ratio,underwater color quality assessment,and underwater image quality metric is 0.8906,29.0761,0.4454 and 3.1810 respectively.In terms of subjective evaluation and objective evaluation indicators,the experimental results of the algorithm in this paper are better than the comparison algorithms.关键词
水下图像增强/生成对抗网络/注意力机制/多尺度Key words
underwater image enhancement/generative adversarial networks/attention mechanism/multi-scale分类
信息技术与安全科学引用本文复制引用
陈海秀,陆康,何珊珊,刘磊,颜秋叙..多尺度注意力网络的水下图像增强算法[J].机械科学与技术,2025,44(3):505-512,8.基金项目
国家自然科学基金项目(61302189)与教育部产学合作协同育人项目(202101159003) （61302189）
