Integrated multi-branch neural network dehazing algorithm

Recently, the field of image dehazing has achieved rapid development. End-to-end deep learning algorithms achieve good performance. However, we observe that many algorithms perform poorly on the task of image dehazing in high-resolution complex environments. In this paper, aiming at the problems of detail loss, edge blurring and regional haze legacy in the process of dehazing non-uniform haze images with high resolution by existing algorithms, an end-to-end dehazing model integrating multiple neural networks is proposed. To be specific, we introduce a multiple neural network to deal with the aforementioned problems. Our proposed structure consists of three branches: the encoder-decoder network is constructed by transfer learning to reconstruct haze-free features; Full-resolution residual extraction network helps to avoid loss of details in downsampling layers; Swin-transformer scale extraction network makes the network receptive field richer. Then, their different features are then mapped through a learnable fusion tail. Extensive experimental results on real datasets show that our proposed ensemble multi-branch dehazing model has better recovery ability and good generalization ability on multiple datasets.