In this letter, we present a novel integrated feature that incorporates traditional parameters, and adopt a parallel cascading fashion network HazeNet for enhancing image quality. Our unified feature is a complete integration, and its role is to directly describe the effects of haze. In HazeNet, we design two separate structures including backbone and auxiliary networks to extract feature map. Backbone network is responsible for extracting high-level feature map, and low-level feature learned by the auxiliary network can be interpreted as fine-grained feature. After cascading two features with different accuracy, final performance can be effectively improved. Extensive experimental results on both synthetic datasets and real-world images prove the superiority of the proposed method, and demonstrate more favorable performance compared with the existing state-of-art methods.