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A two-branch remote sensing image dehazing network based on hierarchical feature interaction and enhanced receptive field

  • role： First author第一作者
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China

  • Email：sunhang0418@whu.edu.cn
  • Introduction：孙航，研究方向为计算机视觉、图像去雾、图像增强。E-mail： sunhang0418@whu.edu.cn
SUN Hang13,  
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

FANG Shuailing1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China

  • Email：zp_dan@ctgu.edu.cn
  • Introduction：但志平，研究方向为计算机视觉、自然语言处理。E-mail： zp_dan@ctgu.edu.cn
DAN Zhiping13*,  
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hubei Engineering Technology Research Center for Farmland Environmental, China Three Gorges University, Yichang, 443002, China

REN Dong12,  
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hubei Engineering Technology Research Center for Farmland Environmental, China Three Gorges University, Yichang, 443002, China

YU Mei12,  
  • Affiliation：

    College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China

SUN Shuifa13

résumé

In recent years, deep learning-based dehazing methods have achieved remarkable results in the field of image dehazing. However, most dehazing methods based on U-shaped networks directly transfer the features of the encoding layer to the corresponding decoding layers, which lacks information interaction between the low- and high-level features. Meanwhile, the network model designed based on the U-shaped structure may destroy the detailed information important for the restored image in the process of downsampling. As a result, the restored clear image lacks detailed texture and structure information. In addition, the dehazing method based on non-U-shaped network has limited receptive field, which hinders its capability to effectively utilize contextual information. As a result, these methods cannot achieve ideal dehazing results in remote sensing images with large scene scale changes. Therefore, this study proposes a two-branch remote sensing image dehazing network based on hierarchical feature interaction and enhanced receptive field. This network includes hierarchical feature interaction sub-net and multi-scale information extraction sub-net. The hierarchical feature interaction sub-net uses the hierarchical feature interaction fusion module to introduce semantic information into low-level features and spatial details into high-level features layer by layer. This way enhances the information interaction between features at different levels in the encoding layer. The multi-scale information extraction sub-net uses the multi-scale residual dilated convolution module to fuse the features of different receptive fields for obtaining contextual information, which is crucial for remote sensing image dehazing. The experiment on two public datasets show that the dehazing method proposed in this study achieves the best evaluation compared with the existing nine excellent dehazing algorithms. Among them, in the three sub-test sets of the public remote sensing dataset Haze1k, the quantitative index PSNR values of this study reach 27.362, 28.171, and 25.137 dB. In the two sub-test sets of the public remote sensing dataset RICE, the quantitative index PSNR values of this study reach 37.79 and 35.367 dB. In addition, the method proposed in this study is the closest to ground truth in terms of subjective visual qualities such as color, saturation, and sharpness, while still achieving the dehazing effect. The following conclusions can be drawn: (1) through the proposed hierarchical feature interaction fusion module, the deep semantic information in the coding stage is gradually interactively fused with the shallow detailed texture information, which enhances the expressive ability of the network and restores clear images with higher quality. (2) Through the multi-scale residual dilated convolution module, the dehazing network proposed in this study can increase the receptive field of the network without changing the size of the feature map. The contextual information of different scales can also be fused. (3) In two public remote sensing image dehazing datasets, namely, Haze1k and RICE, the dehazing method proposed in this study outperforms nine recently proposed excellent dehazing algorithms in terms of objective evaluation indexes and subjective visual effects.

mots-clés

deep learning;remote sensing image dehazing;hierarchical feature interaction;receptive field;two-branch

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