层级特征交互与增强感受野双分支遥感图像去雾网络  

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

作　　者：孙航 方帅领 但志平[1,3] 任东[1,2] 余梅[1,2] 孙水发[1,3] SUN Hang;FANG Shuailing;DAN Zhiping;REN Dong;YU Mei;SUN Shuifa(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;Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,China Three Gorges University,Yichang,443002,China)

机构地区：[1]三峡大学计算机与信息学院,宜昌443002 [2]三峡大学湖北省农田环境监测工程技术研究中心,宜昌443002 [3]三峡大学湖北省水电工程智能视觉监测重点实验室,宜昌443002

出　　处：《遥感学报》2023年第12期2831-2846,共16页NATIONAL REMOTE SENSING BULLETIN

基　　金：湖北省自然科学基金青年基金(编号:2021CFB004);国家重点研发计划(编号:2016YFD0800902);国家自然科学基金面上项目(编号:61871258)。

摘　　要：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 multiscale 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

关 键 词：深度学习 遥感图像去雾 层级特征交互 感受野 双分支 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]