光学精密工程2025，Vol.33Issue(7)：1141-1151,11.DOI:10.37188/OPE.20253307.1141
基于多分支残差注意力网络的水下图像增强
Underwater image enhancement based on multi-branch residual attention network
摘要
Abstract
To address color distortion,low contrast,and blurred details in underwater images,a novel en-hancement algorithm based on a multi-branch residual attention network is proposed.Initially,a multi-branch color enhancement module is integrated before and after the encoder and decoder to adaptively cor-rect image color deviations.Subsequently,a residual attention module is incorporated at the network's bottleneck to mitigate feature loss between the encoder and decoder,thereby improving image detail pres-ervation.A composite feature loss function is employed to facilitate comprehensive feature learning and ef-fective retention of edge information.Experimental results demonstrate that the proposed algorithm achieves superior performance in both subjective perception and objective evaluation metrics.Specifically,the average peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)on the LUSI test set reach 27.420 dB and 0.885,representing improvements of 3.9%and 0.8%,respectively,over the next best method.On the EVUP test set,PSNR and SSIM attain 26.159 dB and 0.851,with enhance-ments of 3.3%and 1.3%,respectively.These results confirm the algorithm's effectiveness and robust-ness in underwater image quality enhancement,offering a valuable approach for image analysis in underwa-ter engineering applications.关键词
水下图像增强/深度学习/残差注意力模块/多分支色彩增强模块/注意力机制/联合损失函数Key words
underwater image enhancement/deep learning/residual attention module/multi-branch col-or enhancement module/attention mechanism/joint loss function分类
信息技术与安全科学引用本文复制引用
程竹明,李佳轩,黄三傲,韩立超,王培珍..基于多分支残差注意力网络的水下图像增强[J].光学精密工程,2025,33(7):1141-1151,11.基金项目
国家自然科学基金资助项目(No.51574004) （No.51574004）
安徽省高校自然科学基金重点项目(No.KJ2019A0085) （No.KJ2019A0085）
