## 1 Introduction
For image-dehazing tasks, the hazy-free image domain possesses more information than the hazy image domain, which means that the information between the two image domains is unequal; (ii) the feature extraction part of CycleGAN is less suitable for dehazing tasks, because, for the image-to-image translation task, it focuses on extracting the low-frequency features of the image, while the extraction of the high-frequency features of the image is slightly insufficient. However, for image-dehazing tasks, it is ideal if the high-frequency features of haze-free images are rich enough, as it is necessary to extract enough high-frequency features from haze images to better reconstruct the haze-free image; (iii) for the image-to-image translation task, the exchange of image feature information is necessary; the encoding-residual-decoding structure without cross-layer skip connection can achieve the exchange of feature information, resulting in the exchange of the source domain image and target domain image based on the extracted source domain image features. However, for image-dehazing tasks, the recovery of image feature information is needed; this no-cross-layer skip connection structure is not suitable, because the image feature information obtained by the encoding module is not directly provided to the decoding module for recovering clear images. Therefore, it is necessary to use the cross-layer skip connection to provide the features of different scales obtained by the encoding module directly to the decoding module for recovering images in order to avoid the problem of distortion, loss of image details, and poor retention of image features in haze-free images. To solve the above problems, this paper reports on research work based on CycleGAN from two aspects of network structure and loss function, and it innovatively proposes CL-GAN to achieve low-frequency feature retention and high-frequency feature recovery to better recover the high-frequency features in the haze-free image and retain the low-frequency features in the original image, thus improving the quality of the haze-free image. The main contributions of this paper are as follows:  
1. We propose CL-GAN based on CycleGAN, which achieves one-sided image translation by introducing contrastive learning [28; 29; 30; 31], to address the adverse effects of information inequality between haze and haze-free images in the image-dehazing task in order to achieve a visually better image-dehazing algorithm;
2. We innovatively propose the feature-fusion module (FFM) based on multi-scale skip connection (MSC) to achieve the fusion between different-scale features, which enables the continuous fusion of the features of different scales extracted by the feature-extraction module when they are passed in the decoding module, thus effectively improving the utilization of feature information;
3. Compared with existing methods, the method proposed in this paper achieves better image-dehazing results in both synthetic and real-world haze image datasets. The remainder of the paper is organized as follows: Section 2 provides a brief overview of the related work; Section 3 details the method proposed in this paper; Section 4 presents the experimental results and analysis of the method proposed in this paper; finally, Section 5 concludes this paper.