Dehazeformer:Nonhomogeneous Image Dehazing With Collaborative Global-local Network
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.
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