Image defogging network based on dual learning

Aiming at the degradation problem of the images collected by the optical imaging equipment in hazy days, a Dual Dehazing Network based on dual learning from the source domain to the target domain is proposed to realize the image dehazing. First, the network learns the bilateral mapping relationship between the hazy image and the haze-free image by using the Dual Generative Adversarial Network, and obtains the preliminary dehazing result. Then the pre-training model is used to calculate the feature vector of the dehazed image and the real haze-free image in the feature space. The Euclidean distance is used as the loss function to minimize the distance between the feature vectors to ensure that the dehazed image is close to the real haze-free image at the feature level. The experimental results show that the dehazing results obtained by the Dual Dehazing network have higher peak signal-to-noise ratio and lower color difference, and can effectively preserve the structural information of the image.