DPAFNet:Dual Path Attention Fusion Network for Single Image Deraining
Bingcai Wei
Rainy weather will have a significant impact on the regular operation of the
imaging system. Based on this premise, image rain removal has always been a
popular branch of low-level visual tasks, especially methods using deep neural
networks. However, most neural networks are but-branched, such as only using
convolutional neural networks or Transformers, which is unfavourable for the
multidimensional fusion of image features. In order to solve this problem, this
paper proposes a dual-branch attention fusion network. Firstly, a two-branch
network structure is proposed. Secondly, an attention fusion module is proposed
to selectively fuse the features extracted by the two branches rather than
simply adding them. Finally, complete ablation experiments and sufficient
comparison experiments prove the rationality and effectiveness of the proposed
method.