## 2 Related Work
[18] proposed using the Al-Alaoui operator to enhance the image through convolution filtering, and based on this, to carry out dehazing. Combining the two theories to process different image regions separately is also common. [PERSON] et al. [19] proposed a pixel-wise alpha blending method for estimating the transmission map, where the transmissions estimated from the dark channel prior and the proposed bright channel prior are effectively blended into one transmission map, guided by a brightness-aware weights map. [PERSON] et al. [20] combined and corrected the two estimation methods of transmittance by assigning different weights. These methods, which study the imaging principle of the object, have a significantly better dehazing effect than image enhancement-based methods. However, a priori assumptions need to be utilized for the evaluation of transmittance and global atmospheric light. These assumptions will fail in certain situations, such as image distortion when processing large sky areas. Deep learning-based methods can use a large amount of data to learn the mapping relationships between haze-free images. [PERSON] et al. proposed the DehazeNet network [21] to estimate transmittance and atmospheric light values and perform dehazing of images based on the atmospheric scattering model. [PERSON] et al. first proposed the end-to-end dehazing network, AOD-Net [22]. To address the problem of grid artifacts easily caused by dilated convolution, [PERSON] et al. proposed an end-to-end gated context aggregation network, GCA-Net [23]. [PERSON] et al. proposed an image dehazing network with an encoding and decoding structure based on a conditional generative adversarial network (GAN) [24]. [PERSON] et al. proposed Cycle-Dehaze [25], combining cyclic consistency loss and perceptual loss to train the network. [PERSON] et al. proposed an encoder-decoder based on GAN [26] to further improve the network dehazing capability by learning different high- and low-frequency information of the image. [PERSON] proposed a novel method for generating untargeted adversarial examples using GAN in an unrestricted black box environment [27]. [PERSON] et al. proposed DehazeFormer for image dehazing, based on the Transformer model [28]. These methods have achieved significant results by leveraging the learning capability of neural networks. However, they heavily depend on extensive natural scene datasets, and it is easy to overfit during the learning process, resulting in a general effect in real scene dehazing. In conclusion, although image enhancement-based methods are simple, they can easily lose image detail information and exhibit poor robustness. Physical model-based methods can analyze the reasons for image degradation, but in practical applications, they can suffer from parameter estimation biases, which lead to incomplete image dehazing. Deep learning-based methods can learn from a large amount of image data, understand image information more accurately, and exhibit strong adaptability. Therefore, we choose deep learning-based methods for multi-scene image dehazing. However, this type of method requires a large amount of data for training. It is difficult to obtain paired datasets, and using synthetic datasets can easily lead to overfitting. Additionally, problems such as insufficient use of feature information can result in blurred edges and loss of fine details in dehazed images. To address these issues, we propose an improved model of cyclic generative adversarial networks. The network is based on CycleGAN for image dehazing, using unpaired datasets for model training, which can alleviate the overfitting problem to some extent. A weight allocation mechanism is introduced into feature fusion to make full use of feature information and retain image details after dehazing.