Aiming at the problems that the restored images of existing dehazing algorithms are prone to color distortion and details lost,an end-to-end image dehazing method based on cycle-consistent generative adversarial networks (CycleGAN) is proposed,which does not rely on the constriction of atmospheric scattering model.The whole network generator adopts the Encoder-Decoder framework,and in order to effectively learn the mapping relationship between hazy images and clear images,the enhanced high-frequency loss and feature loss functions are constructed in the training optimization objective by combining the image attributes, which realizes the feature identification of different data domains and further ensures the texture structure of images. In addition,a two-stage learning strategy is proposed to constrict the color consistency between restored images and their corresponding clear images.Firstly,the improved CycleGAN is learned by weakly-supervised training with unpaired datasets.Then in the second stage,the forward generator network is trained in a strongly-supervised manner with some paired datasets,which improves the stability of dehazing network while making the restoration effect closer to the real clear image style.The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of the proposed dehazing method are improved by 12.43% and 5.53%,respectively,compared with the similar CycleGAN algorithm, and the effectiveness of the proposed method is also verified by comparing the results of visual effect and quantitative index with other methods.