液晶与显示2025，Vol.40Issue(10)：1557-1567,11.DOI:10.37188/CJLCD.2025-0163
基于多级特征编码与双分支引导重构的图像去雾网络
Hierarchical encoding and dual-branch guided reconstruction network for image dehazing
摘要
Abstract
To address the limitations of existing dehazing methods in detail restoration and global modeling,this paper proposes HEDGR-Net,a deep dehazing network based on hierarchical encoding and dual-branch guided reconstruction.HEDGR-Net adopts structure-enhanced convolution and non-local attention in the encoder to capture multi-scale structural and contextual features.In the decoder,a dual-branch design with a global context-guided module integrates local detail recovery and global semantic consistency.At the output stage,an attention-enhanced residual fusion module combines reconstructed and input features to refine image quality.A composite loss combining L2,SSIM,and TV is further employed to balance pixel fidelity,structural similarity,and smoothness.On the synthetic SOTS-outdoor dataset,HEDGR-Net achieves 27.54 dB PSNR and 0.957 0 SSIM.On the real RTTS dataset,it obtains 26.99 BRISQUE and 8.77 Entropy.Compared with AOD-Net,HEDGR-Net improves PSNR by 21.7%,SSIM by 5.0%,reduces BRISQUE by 19.2%,and increases Entropy by 3.9%,showing clear advantages across multiple metrics.The proposed method enhances detail restoration and global brightness consistency,effectively overcoming the limitations of traditional dehazing methods such as incomplete haze removal and color distortion.关键词
计算机视觉/图像去雾/多级特征融合/双分支结构/注意力机制Key words
computer vision/image dehazing/multi-level feature fusion/dual-branch structure/attention mechanisms分类
信息技术与安全科学引用本文复制引用
张鑫琪,田莹,李义荣..基于多级特征编码与双分支引导重构的图像去雾网络[J].液晶与显示,2025,40(10):1557-1567,11.基金项目
辽宁省教育厅资助项目(No.LJ212410146058)Supported by Project of Education Department of Liaoning Province(No.LJ212410146058) （No.LJ212410146058）
