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Received：06 July 2022，
Published：07 December 2023
移动端阅览
近年来，深度学习的去雾方法在图像去雾领域取得了显著的成绩。然而，大多数基于U型网络的去雾方法将编码层特征直接传递到对应解码层，缺乏浅层和深层特征之间的信息交互。此外，基于非U型网络的去雾方法存在感受野受限问题，无法有效的利用上下文信息。从而导致这些方法在场景尺度变化较大的遥感图像去雾中无法取得理想效果。为此，本文提出了一种层级特征交互与增强感受野的双分支遥感图像去雾网络，该方法包含层级特征交互子网和多尺度信息提取子网。其中，层级特征交互子网利用层级特征交互融合模块，逐层的在浅层特征中引入语义信息，深层特征中引入空间细节信息，从而增强编码层中不同层级特征之间的信息交互。多尺度信息提取子网利用多尺度残差空洞卷积模块，融合不同感受野的特征，从而获取对于遥感图像去雾至关重要的上下文信息。在两个公开数据集上的实验结果表明，本文提出的去雾方法相比现有的9种优秀的去雾算法，取得了最好的客观评价指标和视觉效果。
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.
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