## 1 Introduction  
When there is the influence of haze, suspended particles in the air can refract and scatter light, thus attenuating the light intensity reflected from the target to be observed. At the same time, the attenuated reflected light mixes with the light refracted and scattered by the suspended particles, resulting in a substantial decrease in the clarity and contrast of the image captured by cameras, a large amount of detailed information about the target is lost, and the color of the target is biased, making it impossible to accurately extract the geometric and color characteristics of the target [1, 2] and limiting the applications of automatic driving, satellite remote sensing, traffic monitoring [3, 4], etc. Therefore,it is of great significance and application value to study the method of removing haze from images. In recent years, image dehazing has become a popular research topic in academia and industry. Early dehazing algorithms were mainly based on the atmospheric scattering model [5], which can be expressed as  
\[I(x)=J(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(I(x)\) denotes the haze image, \(J(x)\) is the corresponding haze-free image, \(t(x)\) denotes the medium transmission map, and \(A\) denotes the global atmospheric light. By estimating the parameters \(A\) and \(t(x)\), the haze image is recovered, obtaining the corresponding haze-free image. However, owing to the complex physical factors affecting the dehazing effect, the atmospheric scattering model cannot be accurately established to achieve the recovery of haze images in complex scenes. With the rapid development of deep learning technology, convolutional neural networks (CNN) have been heavily applied in image processing [6; 7; 8; 9; 10], especially in image dehazing. Existing deep-learning-based dehazing algorithms have mainly been studied in two aspects. On one hand, deep learning is used to estimate parameters, such as transmittance and global atmospheric light, of the atmospheric scattering model [11; 12; 13; 14], where haze-free images are projected from haze images; on the other hand, according to the idea of image-to-image translation, the atmospheric scattering model is directly ignored and the image-dehazing task is regarded as an image-to-image translation task to find the mapping between haze and haze-free images in order to achieve end-to-end dehazing [15; 16]. However, the existing dehazing algorithms based on convolutional neural networks are based on supervised learning [17; 18; 19; 20; 21; 22], which requires a large amount of paired data, and the lack of data will affect the performance of the dehazing algorithm. Meanwhile, owing to the continuous changes in light intensity, it is difficult to acquire the corresponding haze and haze-free images in the same scene, which means that paired image data are difficult to obtain; thus, research on unsupervised dehazing algorithms is very meaningful. The proposal of Generative Adversarial Networks (GANs) [23] has greatly contributed to the further development of unsupervised learning. In the field of unsupervised image-to-image translation, Cycle-Consistent Adversarial Networks (CycleGAN) [24] showed the potential for GANs in unsupervised image-to-image translation by introducing GANs, and many CycleGAN-based image-to-image translation algorithms [25; 26; 27] have subsequently been proposed. However, the results obtained from the direct use of CycleGAN for image-dehazing tasks are not very good, and the main issues are as follows: (i) CycleGAN assumes a bidirectional mapping between two image domains, which means that the information possessed by two image domains is equal.