Multi-level fusion dehazing network based on learning of hazy layers

Aiming at the problems such as color cast and incomplete haze removal in dehazing algorithms, a multi-level feature fusion network based on the learning of hazy layers is proposed for single image dehazing. Firstly, a difference image between hazy image and haze-free image is defined as the hazy layer via atmospheric scattering model, and the effective estimation of the hazy layer could be used to optimize dehazing effect. Then, an end-to-end network model is designed, which mainly includes a hazy layer estimation module and an image restoration module. In hazy layer estimation module, the low-level and high-level features of image are extracted through feature extraction blocks, and a multi-level fusion strategy is used to add the features of different levels pixel by pixel to achieve feature fusion. The fused hazy layer contains both local and global information. Finally, the hazy layer is directly subtracted from hazy image to achieve the effective restoration of haze-free image according to image restoration module. Experiments show that the proposed algorithm can obtain clear and natural results, and the color cast phenomenon is effectively avoided compared with existing dehazing methods. The objective evaluation indicators on synthetic images and real images further verify the effectiveness of the proposed algorithm.