## 1 Introduction
The algorithm has high efficiency and natural color restoration of the image, but the restoration effect of images with dense haze needs to be improved. Non-physical model dehazing algorithms are based on image enhancement methods, such as the retinex dehazing algorithm based on color constancy [4], the histogram equalization algorithm for balancing pixel distribution [5], and wavelet and homomorphic filtering algorithms. Traditional algorithms often have limitations. Meanwhile, learning-based dehazing algorithms can also be divided into two parts: parameter estimation and direct restoration. Parameter estimation methods use neural networks to estimate \(t(x)\) and \(A\) for dehazing, and deep learning-based parameter estimation is generally more accurate than non-deep learning-based physical models, such as MSCNN [6], DehazeNet [7], and DCPDN [8]. MSCNN combines coarse and fine scale networks to obtain more accurate \(t(x)\). DCPDN uses two networks to estimate \(t(x)\) and \(A\) separately. Direct restoration methods in learning-based dehazing algorithms use neural networks to directly estimate dehazed output from hazy input images, such as FD-GAN [9], GridDehazeNet [10], FFA-Net [11], and DehazeFormer [12]. FD-GAN uses a fused discriminator that takes frequency information as prior knowledge. GridDehazeNet consists of preprocessing, backbone, and postprocessing modules, introducing attention-based multiscale estimation. FFA-Net introduces feature attention modules with channel and pixel attention mechanisms. DehazeFormer is a transformer-based architecture for dehazing. In the icing wind tunnel environment, the concentration of fog is usually high. To address this issue, we estimate the transmission rate and atmospheric light values through network learning based on the inverse problem of image formation. We then apply the atmospheric scattering model to perform dehazing, strictly following the physically driven scattering model. This approach results in better dehazing effects. The At-DH [13], a GAN model-based algorithm, achieved a significant result in the NTIRE 2019 challenge [14], showing that the generative adversarial network (GAN) has great potential in the field of real image restoration, such as image dehazing. Inspired by that, we combine this strategy with image dehazing to generate more realistic dehazed images. We propose a high-resolution dense-connection GAN model specifically designed for dehazing images in the icing wind tunnel environment. The main contributions of this paper are as follows:  
1. We proposed the development of a novel generative image super-resolution dehazing model, which is suitable for the icing wind tunnel environment. We validated the model using real-world images captured in the icing environment, and the results showed excellent dehazing performance. 2. The proposed model involves the incorporation of sub-pixel convolution and instance normalization into the network architecture to generate high-resolution dehazed images while preserving the structural information of ice on the wings. Sub-pixel convolution is employed to mitigate artifacts arising from traditional deconvolution, while instance normalization is used to enhance image style transformation. The model enables the capture of both content and style information from hazy and haze-free images, leading to a more effective restoration of the haze-free appearance.