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浙江大学学报(工学版)  2026, Vol. 60 Issue (6): 1221-1230    DOI: 10.3785/j.issn.1008-973X.2026.06.009
计算机技术     
基于动态频域调制的交互式图像去雾网络
杨燕(),宋鑫钰
兰州交通大学 电子与信息工程学院,甘肃 兰州 730070
Interactive image dehazing network based on dynamic frequency-domain modulation
Yan YANG(),Xinyu SONG
School of Electronic and Information Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China
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摘要:

针对现有图像去雾方法在多样性复杂雾气场景中的不足,提出基于动态频域调制的交互式双分支图像去雾网络. 构建由全局语义建模分支与残差细节建模分支组成的并行编码器,分别捕捉图像的全局语义信息与局部纹理特征,提出自适应交叉融合模块,实现跨分支特征的动态交互,提升特征协同能力. 设计动态频域增强模块,强化模型对高频细节与复杂雾气区域的响应能力. 在解码器中引入边缘辅助监督,与频域增强形成互补约束,引导网络关注图像轮廓,提升细节恢复能力及视觉清晰度. 在RESIDE、NH-HAZE、O-HAZE和I-HAZE数据集上的实验结果表明,所提方法具备更强的结构还原能力与视觉一致性,在I-HAZE数据集上,PSNR和SSIM分别达到24.93 dB和0.812 6,较次优方法分别提升了2.02 dB和0.049 6.

关键词: 图像去雾双分支结构特征交互频域调制边缘引导    
Abstract:

An interactive dual-branch image dehazing network based on dynamic frequency-domain modulation was proposed in order to address the limitation of existing image dehazing method in diverse and complex haze scene. A parallel encoder composed of a global semantic modeling branch and a residual detail modeling branch was constructed to capture global semantic information and local texture feature, respectively. An adaptive cross fusion module was introduced to enable dynamic interaction between cross-branch feature and enhance cross-feature collaboration capability. A dynamic frequency-domain enhancement module was designed to strengthen the response of the model to high-frequency detail and complex haze region. An edge-guided auxiliary supervision mechanism was introduced in the decoder, which formed complementary constraint with frequency-domain enhancement to guide the network to focus on the image contour. Then detail restoration and visual clarity were improved. The experimental results on the RESIDE, NH-HAZE, O-HAZE and I-HAZE datasets demonstrate that the proposed method achieves stronger structure restoration capability and better visual consistency. PSNR and SSIM reached 24.93 dB and 0.812 6 on the I-HAZE dataset, respectively, which were improved by 2.02 dB and 0.0496 compared with the second-best method.

Key words: image dehazing    dual-branch architecture    feature interaction    frequency-domain modulation    edge guidance
收稿日期: 2025-07-29 出版日期: 2026-05-06
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目(62063014);甘肃省高等学校产业支撑计划资助项目(2021CYZC-04);甘肃省优秀研究生“创新之星”项目(2025CXZX-681).
作者简介: 杨燕(1972—),女,教授,博士,从事计算机视觉、数字图像处理研究. orcid.org/0000-0001-5338-0762. E-mail:yangyantd@mail.lzjtu.cn
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引用本文:

杨燕,宋鑫钰. 基于动态频域调制的交互式图像去雾网络[J]. 浙江大学学报(工学版), 2026, 60(6): 1221-1230.

Yan YANG,Xinyu SONG. Interactive image dehazing network based on dynamic frequency-domain modulation. Journal of ZheJiang University (Engineering Science), 2026, 60(6): 1221-1230.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2026.06.009        https://www.zjujournals.com/eng/CN/Y2026/V60/I6/1221

图 1  基于动态频域调制的交互式图像去雾网络整体结构
图 2  动态频域增强模块
图 3  多尺度特征增强编码块
图 4  自适应交叉融合模块
图 5  多尺度通道注意力解码块
图 6  不同方法在SOTS-outdoor数据集上的去雾结果
图 7  不同方法在NH-HAZE数据集上的去雾结果
图 8  不同方法在O-HAZE数据集上的去雾结果
图 9  不同方法在I-HAZE数据集上的去雾结果
图 10  不同方法在真实图像上的去雾结果
方法SOTS-outdoorNH-HAZEO-HAZEI-HAZE
PSNR/dBSSIMPSNR/dBSSIMPSNR/dBSSIMPSNR/dBSSIM
GCA-Net[29]31.980.914519.680.701721.630.774219.190.6529
Dehazeformer[14]33.370.892822.070.819625.310.783720.670.6792
C2PNet[30]32.270.807318.510.741620.450.706221.790.7285
DEA-Net[31]33.530.985420.200.761522.730.753720.350.6944
DehazeXL[32]28.560.857717.320.706024.950.805322.910.7630
本文方法33.910.963123.710.829726.540.821124.930.8126
表 1  不同去雾方法在各数据集上的PSNR和SSIM结果
方法NH-HAZEO-HAZEI-HAZE
PSNR/dBSSIMPSNR/dBSSIMPSNR/dBSSIM
Model A16.310.694819.130.676719.930.6739
Model B17.860.714321.890.681820.770.6943
Model C19.880.761424.260.715621.230.7725
Model D20.270.772924.070.739122.640.7683
Model E21.930.795525.560.756323.730.7935
本文方法23.710.829726.540.821124.930.8126
表 2  消融实验的客观指标对比
图 11  在O-HAZE数据集上的消融实验主观可视化对比
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