## 2 Related Work
### Image Dehazing
[39] proposed the Deep Fully Convolutional Regression Network (DFCRN) to estimate the transmittance in atmospheric scattering models while using the ReLU activation function and batch normalization layer to accelerate network convergence and prevent network overfitting. [PERSON] et al. [14] proposed a lightweight CNN-based end-to-end convolutional neural network, AOD-Net, to combine the transmittance and atmospheric light value parameters in the atmospheric scattering model into one parameter for estimation with the network, which effectively reduces the reconstruction error of the atmospheric scattering model and improves the quality of the haze-free image. [PERSON] et al. [40] proposed a Densely Connected Pyramid Dehazing Network (DCPDN) based on generative adversarial networks to estimate the transmittance parameters of the atmospheric scattering model using a densely connected module with a multi-layer pyramid structure, and then derived the recovered haze-free images. [PERSON] et al. [41] proposed a multi-model fusion image-dehazing network, which generates multi-modal attention features from different layers of convolutional features and incorporates them into a fusion model combined with the atmospheric scattering model to derive haze-free images, effectively improving the overall performance of image dehazing. The other is to use neural network models, such as convolutional neural networks and generative adversarial networks, to directly determine the mapping between the haze image and the haze-free image for image dehazing. [PERSON] et al. [42] proposed an image-dehazing network based on a conditional generative adversarial network (cGAN) to generate recovered haze-free images directly from the input haze images without using an atmospheric scattering model by introducing a VGG feature extraction module and an L1 regularized gradient prior. [PERSON] et al. [43] proposed a Gated Fusion Network (GFN) to first obtain corresponding white balance feature maps, contrast enhancement feature maps, and gamma correction feature maps based on haze images, then obtained the confidence coefficient of each feature map by training the network, and finally recovered haze-free images by fusing the most important feature information among them. [PERSON] et al. [15] proposed Cycle-Dehaze based on cyclic generative adversarial networks; by combining cycle consistency loss and perceptual loss to train the network, they could obtain better visually haze-free images. [PERSON] et al. [44] proposed an end-to-end Gated Context Aggregation Network (GCANet), which uses a gated sub-network to fuse image features from different levels and solves the artifact prob lem in dehazing tasks by using smoothed convolution instead of extended convolution, effectively improving the dehazing effect. [PERSON] et al. [45] proposed an end-to-end Feature Fusion Attention Network (FFA-Net), which contains a feature attention module that extends the feature representation capability of deep neural networks by combining the channel attention mechanism and the pixel attention mechanism to make it more flexible in processing different types of information, thus improving the visual performance of recovered haze-free images. [PERSON] et al. [46] proposed an end-to-end trainable, densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image to solve color distortion, artifacts, and insufficient haze removal. [PERSON] et al. [47] proposed a generative adversarial network based on the deep symmetric Encoder-Decoder architecture for removing dense haze to solve dense haze being unevenly distributed on the images and the deep convolutional dehazing network relying too greatly on large-scale datasets. [PERSON] et al. [48] proposed an image-dehazing model built with a convolutional neural network and a transformer, called Transformer for Image Dehazing (TID), to effectively improve the quality of the restored image.