Dehazeformer: Nonhomogeneous Image Dehazing With Collaborative Global-local Network

In recent years, image dehazing methods based on convolutional neural network (CNN) have made remarkable progress in synthetic datasets, but the local receptive field of convolution operation is difficult to effectively 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 challenges. 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 complexity 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 reduce computational complexity. Besides, the convolutional neural network branch is used to obtain local information to recover texture details. Extensive experiments in the real nonhomogeneous haze scene show that the proposed method achieves excellent performance in both objective evaluation and subjective visual effects. © 2024 Science Press. All rights reserved.