Three Subnets Image Dehazing Method Based on Transfer Learning

Most image dehazing algorithms perform well in one or several homogeneous hazy map datasets, but process poor performance in datasets with different styles or nonhomogeneous hazy map datasets. Meanwhile, in practical application, the algorithm will be limited in model scenes due to poor model generalization ability. In view of the above situation, a Convolutional Neural Network (CNN) based on transfer learning is proposed to alleviate problems such as nonhomogeneous hazy map dehazing and defective generalization ability. ImageNet pre-trained model parameters are utilized as the initial parameters of the transfer learning model. In order to accelerate the convergence rate of model training, the algorithm is able to adapt quickly to different datasets. The model is composed of three subnets: residual feature sub network, local network and the overall feature extraction of feature extraction sub network. The model is ensured by the combination of three subnets to extract features from both the whole image feature and the local image feature, and achieves excellent dehazing effect in real hazy scenes (homogeneous and nonhomogeneous haze). In summary, the proposed method improves the model efficiency, haze removal quality and convenience of hazy map scene selection. To quantitatively and qualitatively measure the performance of the model, experiments are performed on NTIRE2020 and NTIRE2021, which are nonhomogeneous hazy map datasets with high haze removal difficulty. Experimental results show that the three-subnets model achieves outstanding results in both subjective and objective evaluation metrics. Unsatisfactory generalization performance of the algorithm and training difficulty are improved in small datasets. The architecture of three subnets can be widely utilized in small-scale datasets and changeable scene image dehazing projects.