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Convolutional neural networks have achieved remarkable success in single image dehazing tasks, and previous studies verified the dehazing performance of the U-shaped framework. However, most existing U-shaped architecture dehazing networks still face challenges in sufficiently dealing with a large area of haze with low visibility. In this paper, we propose a novel dehazing network named Double U-Net(DU-Net). Specifically, to reduce the interference of haze features in the encoder to the recovery stage when skip-connecting to the decoder directly, we develop a new architecture firstly, which is composed of an extended encoder–decoder. Besides, the hierarchical depth-wise convolution block(HDCB) is designed to gradually increase the receptive field by leveraging the depth-wise convolution, enriching the global information. Moreover, we propose a multi-branch interactive fusion(MIF) which achieves efficient cross-branch and cross-channel interaction through parallel multiple 1D convolutions. Extensive experiments on both synthetic and real-world hazy images demonstrate the effectiveness of our proposed method. • We construct a Double U-Net for image dehazing, by extending the encoder–decoder. • We build hierarchical convolution blocks to obtain a multi-scale receptive field. • A branch fusion is proposed to enhance cross-branch and cross-channel interaction. [ABSTRACT FROM AUTHOR]