Coastal zone image dehazing network based on feature fusion and adversarial training

The quality of the images collected by the coastal zone video surveillance equipment is seriously degraded due to the sea fog, which directly affects the analysis of the image. Therefore, the study of the costal image dehazing method is of great significance to the related research of the coastal zone. Costal image has the characteristics of large sky area and monotonous color. The traditional method based on atmospheric scattering physics model is not suitable for this kind of image for block effect and color distortion. In this paper, we introduce the generative adversarial mechanism into sea fog image defogging, and propose a coastal image dehazing network based on it. The proposed model includes a generative network and a discriminative model, and is trained by adversarial mechanism. The generative model is composed of multi-scale feature extraction module and residual connection module. The discriminative network consists of two subnetworks of receptive field of different sizes.