Wavelet dehazeformer network for road traffic image dehazing method

XIA Ping; LI Ziyi; LEI Bangjun; WANG Yudie; TANG Tinglong

doi:10.37188/OPE.20243212.1915

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Wavelet dehazeformer network for road traffic image dehazing method

Information Sciences | 更新时间：2024-07-26

- Wavelet dehazeformer network for road traffic image dehazing method
- Optics and Precision Engineering Vol. 32, Issue 12, Pages: 1915-1928(2024)
- 作者机构：
  
  1.三峡大学水电工程智能视觉监测湖北省重点实验室，湖北宜昌 443002
  2.三峡大学计算机与信息学院，湖北宜昌 443002
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20243212.1915
  CLC： TP183;TP391.41
- Published：25 June 2024，
  
  Received：05 March 2024，
  
  Revised：19 April 2024，
- 稿件说明：
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夏平,李子怡,雷帮军等.小波DehazeFormer网络的道路交通图像去雾[J].光学精密工程,2024,32(12):1915-1928.

XIA Ping,LI Ziyi,LEI Bangjun,et al.Wavelet dehazeformer network for road traffic image dehazing method[J].Optics and Precision Engineering,2024,32(12):1915-1928.
夏平,李子怡,雷帮军等.小波DehazeFormer网络的道路交通图像去雾[J].光学精密工程,2024,32(12):1915-1928. DOI： 10.37188/OPE.20243212.1915.

XIA Ping,LI Ziyi,LEI Bangjun,et al.Wavelet dehazeformer network for road traffic image dehazing method[J].Optics and Precision Engineering,2024,32(12):1915-1928. DOI： 10.37188/OPE.20243212.1915.

摘要

针对道路交通雾图像对比度低、细节丢失、模糊和失真的问题，提出了一种小波DehazeFormer模型的道路交通图像去雾方法。为提升模型去雾能力，构建了编解码结构的小波DehazeFormer网络，编码器以DehazeFormer与选择性核特征融合模块（Selective kernel feature fusion，SKFF）级联作为骨干网络的基本单元，编码部分由三级这样的基本单元构成，以融合图像的原始信息和去雾后的信息，更好地捕获雾图中关键特征；中间特征层采用局部残差结构，并加入卷积注意力机制（Convolutional Block Attention Module，CBAM），对不同级别的特征赋予不同权重，同时融入内容引导注意力混合方案（Content-guided Attention based Mixup Fusion Scheme，CGAFusion），通过学习空间权重来调整特征；解码部分由DehazeFormer和SKFF构成，采用逐点卷积，在保证网络性能同时，减少网络的参数量；跳跃连接引入小波变换，对不同尺度的特征图进行小波分析，获取不同尺度的高、低频特征，放大交通雾图的细节使得复原图像保留纹理；最后，将原始图像和经解码后输出的特征图融合，获取更多的细节信息。实验结果表明，本文方法相比于基线DehazeFormer网络，其PSNR指标在公开数据集中提升1.32以上，在合成数据集中提升0.56，SSIM指标提升了0.015以上，MSE值有较大幅度降低，下降了23.15以上；Entropy指标提升0.06以上。本文去雾算法对提升交通雾图像的对比度、降低雾图模糊和失真及细节丢失等方面均表现出优良的性能，有助于后续道路交通的智能视觉监控与管理。

Abstract

Aiming at challenges such as low contrast， detail loss， blurring， and distortion in foggy images of traffic roads， a road traffic image dehazing method was proposed based on Wavelet DehazeFormer model.To enhance dehazing capability of the model，a Wavelet DehazeFormer network with an encoder-decoder structure was constructed. The encoder employed the DehazeFormer and the Selective Kernel Feature Fusion module （SKFF） as basic units in a cascaded manner. The encoding section consisted of three levels of such basic units to fuse the original information and post-dehazing information， capturing critical features more effectively. The middle feature layer adopted a local residual structure， incorporating the Convolutional Block Attention Module （CBAM） for different weights assigned to features of different levels. Additionally， a Content-guided Attention based Mixup Fusion Scheme （CGAFusion） was introduced to adjust features by learning spatial weights. The decoder comprised DehazeFormer and SKFF， utilizing pointwise convolution to reduce parameter count while maintaining network performance. Jump connections introduced wavelet transform to analyze feature maps of different scales， obtaining high and low-frequency features at various resolutions. This helped amplify details of the traffic fog image for enhanced texture retention in the dehazed image. Finally， the original image and the decoded output feature map were fused to gather more detailed information.Experimental results demonstrate that， in comparison with the baseline DehazeFormer network， the proposed method achieves a PSNR improvement of over 1.32 on public datasets and 0.56 on synthetic datasets. The SSIM metric is increased by 0.015 or more， and there is a substantial reduction in MSE， with a decrease of 23.15 or more. The entropy metric shows an increase of 0.06 or more. The proposed dehazing algorithm exhibits excellent performance in enhancing contrast， reducing fog-induced blurring and distortion， which preserves details in traffic fog images.This contributes to the advancement of intelligent visual surveillance and management in road traffic.

关键词

交通图像去雾小波变换选择性核特征融合内容引导注意力DehazeFormer

Keywords

traffic image dehazingwavelet transformselective kernel feature fusioncontent-guided attentionDehazeFormer

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