自动化学报2024，Vol.50Issue(7)：1333-1344,12.DOI:10.16383/j.aas.c230567
基于全局局部协同的非均匀图像去雾方法
Dehazeformer:Nonhomogeneous Image Dehazing With Collaborative Global-local Network
摘要
Abstract
In recent years,image dehazing methods based on convolutional neural network(CNN)have made re-markable progress in synthetic datasets,but the local receptive field of convolution operation is difficult to effect-ively capture contextual guidance information due to the uneven distribution of haze in the real scene,resulting in the loss of global structure information.Therefore,the image dehazing task in the real scene still faces great chal-lenges.Considering that Transformer has the advantage of capturing long-range semantic information dependency relationships,it can facilitate global structure information reconstruction.However,the high computational com-plexity of the standard Transformer structure hinders its application in image restoration.To solve the problems mentioned above,this paper proposes a double-branch collaborative nonhomogeneous image dehazing network,which is called Dehazeformer and composed of Transformer and convolutional neural network.The Transformer branch is used to extract global structure information,and sparse self-attention modules(SSM)are designed to re-duce computational complexity.Besides,the convolutional neural network branch is used to obtain local informa-tion to recover texture details.Extensive experiments in the real nonhomogeneous haze scene show that the pro-posed method achieves excellent performance in both objective evaluation and subjective visual effects.关键词
图像去雾/卷积神经网络/Transformer/特征融合/稀疏自注意力Key words
Image dehazing/convolutional neural network(CNN)/Transformer/feature fusion/sparse self-attention引用本文复制引用
罗小同,杨汶锦,曲延云,谢源..基于全局局部协同的非均匀图像去雾方法[J].自动化学报,2024,50(7):1333-1344,12.基金项目
国家自然科学基金(62176224,62222602)资助Supported by National Natural Science Foundation of China(62176224,62222602) （62176224,62222602）
