Image haze removal is an important issue in the field of image processing.Deep learning can effectively improve image clarity,but due to the lack of corresponding real haze matching data pairs in the training process,synthetic haze is usually used as the dataset.The existing synthetic haze mostly depends on such parameters as depth information and atmospheric scattering coefficient.To solve the problems of color distortion and incomplete haze removal caused by training on such a dataset,a synthetic haze method based on Cycle Generative Adversarial Network (CycleGAN) is proposed.Through the network,mismatched data pair training is conducted to learn the features of haze images,then real haze features are added to clear pictures,with which matching data pairs are formed,and finally such datasets are used for dehazing training.The results show that these datasets can effectively solve the problems of color distortion and incomplete haze removal.