双注意力引导的细节和结构信息融合图像去雾网络

高继蕊; 李华锋; 张亚飞; 谢明鸿; 李凡

doi:10.12263/DZXB.20211549

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双注意力引导的细节和结构信息融合图像去雾网络

学术论文 | 更新时间：2025-07-02

- 双注意力引导的细节和结构信息融合图像去雾网络
- Dual Attention-Guided Detail and Structure Information Fusion Network for Image Dehazing
- 电子学报 2023年51卷第1期页码：160-171
- 作者机构：
  
  1.昆明理工大学信息工程与自动化学院，云南昆明 650500
  2.云南省人工智能重点实验室，云南昆明 650500
- 作者简介：
  
  [ "高继蕊女，1996年9月出生于云南省临沧市.昆明理工大学信息工程与自动化学院硕士研究生.主要研究方向为计算机视觉与图像处理.E-mail: 2945266153@qq.com" ]
  [ "李华锋男，1983年7月出生于安徽省阜阳市.昆明理工大学信息工程与自动化学院教授，博士生导师.主要研究方向为计算机视觉.E-mail: hfchina99@163.com" ]
  [ "张亚飞（通讯作者）女，1981年5月出生于河南省洛阳市.昆明理工大学信息工程与自动化学院副教授，硕士生导师.主要研究方向为图像处理、模式识别." ]
  [ "谢明鸿男，1976年12月出生于云南省昭通市.昆明理工大学信息工程与自动化学院高级工程师，硕士生导师.主要研究方向为计算机视觉.E-mail: minghongxie@163.com" ]
  [ "李凡男，1986年8月出生于云南省楚雄市.昆明理工大学信息工程与自动化学院副教授，硕士生导师.主要研究方向为计算机视觉.E-mail: 478263823@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62161015)
- DOI：10.12263/DZXB.20211549
  中图分类号： TP399;
- 收稿：2021-11-19，
  
  修回：2022-04-25，
  
  纸质出版：2023-01-25
- 稿件说明：
移动端阅览
高继蕊,李华锋,张亚飞等.双注意力引导的细节和结构信息融合图像去雾网络[J].电子学报,2023,51(01):160-171.

GAO Ji-rui,LI Hua-feng,ZHANG Ya-fei,et al.Dual Attention-Guided Detail and Structure Information Fusion Network for Image Dehazing[J].ACTA ELECTRONICA SINICA,2023,51(01):160-171.
高继蕊,李华锋,张亚飞等.双注意力引导的细节和结构信息融合图像去雾网络[J].电子学报,2023,51(01):160-171. DOI： 10.12263/DZXB.20211549.

GAO Ji-rui,LI Hua-feng,ZHANG Ya-fei,et al.Dual Attention-Guided Detail and Structure Information Fusion Network for Image Dehazing[J].ACTA ELECTRONICA SINICA,2023,51(01):160-171. DOI： 10.12263/DZXB.20211549.

摘要

雾图像结构信息弱化、边缘细节信息丢失，严重影响其在高水平视觉任务的使用.现有大部分去雾方法对图像细节信息的恢复并不理想，影响了图像去雾的整体效果.为此，本文提出一种双注意力引导的细节和结构信息融合去雾网络.该网络主要由空间-通道双注意力联合模块、细节和结构信息融合模块以及多尺度特征重建模块组成.其中，空间-通道双注意力联合模块通过联合空间和通道两个维度的注意力进行特征提取，实现雾图像中细节和结构信息的增强；细节和结构信息融合模块将结构信息和边缘细节信息融合为注意力权重和逆向注意力权重，以进一步增强这两种信息；多尺度特征重建模块将提取到的特征重建为清晰图像.实验结果表明，本文方法的去雾效果在定量评价和视觉效果上均优于对比方法.

Abstract

Haze weakens the structural information of an image and makes the edge information lost

which negatively affects the performance of high-level vision tasks. The details recovered by most existing dehazing methods are unsatisfactory

affecting the overall effect of image dehazing. To this end

this paper proposes a dual-attention guided detail and structure information fusion network composed of spatial-channel dual attention joint module

detail and structure information fusion module

and multi-scale feature reconstruction module. The spatial-channel dual attention joint module performs feature extraction by combining spatial attention and channel attention to enhance details and structural information in the hazy image. The detail and structure information fusion module fuses structure and edge into attention weights and inverse attention weights to further enhance both information. The multi-scale feature reconstruction module reconstructs the extracted features into a clear image. The experiment results show that the dehazing effect of the proposed method is superior to that of the compared methods in both quantitative evaluation and visual effect.

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references

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