## 1 Introduction
This network is composed of two parts: an ultra-dense sub-network (UDSN) and an edge enhancement sub-network (EESN), which together maximize the clarity and noise-free quality of the image edges [15]. Additionally, there are notable research achievements such as the Super-Resolution GANs (SRGANs) proposed by [PERSON] et al. in 2016 [16] and the modified SRGAN network proposed by [PERSON] et al. in 2020 [17]. Through extensive research by scholars, using GANs to accomplish image super-resolution reconstruction has become one of the important research methods in this field. Research on super-resolution reconstruction based on GANs and CNNs has made some progress but still faces significant limitations. Many GAN- and CNN-based super-resolution methods lack smoothness in the generated results, with the reconstructed images often appearing rough, lacking sufficient details and natural transitions. And the generated image sizes are typically small. In particular, traditional GAN models are more proficient at generating low-quality images (e.g., 256 \(\times\) 256), such as UNIT proposed by [PERSON] et al. [18] and DRIT proposed by [PERSON] et al. [19], but they lack the capability to generate high-quality images (e.g., 512 \(\times\) 512 or 1024 \(\times\) 1024), making it difficult to meet the requirements of applications that demand higher precision. Additionally, existing super-resolution networks are susceptible to noise interference, especially in complex environments. Noiseand artifacts often degrade the quality of the generated images, leading to results that are neither clear nor realistic. Lastly, although traditional methods have improved resolution in some cases, the effect on detail recovery and quality enhancement is not ideal, making it difficult to produce high-quality, natural, and refined reconstructed images [20; 21; 22; 23]. To mitigate these limitations, we consider using StyleGAN proposed by [PERSON] et al. to generate high-quality, less noisy, and more realistic super-resolution images. According to the research on StyleGAN, the generator can produce visual features of images at different scales through convolutional layers of varying scales. Lower-level convolutional layers (\(4\times 4\)) generate more global but relatively coarse visual effects, while deeper convolutional layers produce finer visual details. These layers are progressively combined to generate high-quality and smooth images at a 1024 \(\times\) 1024 scale [24; 25]. The network utilizes a set of random vectors and mapping networks to control the style of the generated images, which is more conducive to producing images with higher stylistic diversity. In remote sensing image super-resolution tasks, to ensure that the super-resolution results retain the same semantic information as the original image, it is essential to consider how to extract content information from the low-resolution image to provide to the network. There are some models based on StyleGAN that can provide content information to the generator. Pix2 style2 pix proposes feeding the input image information into the mapping network as an encoder to control the content information of the generated image [26]. Dual-styleGAN, on the other hand, suggests using two mapping networks to separately control the style and content of the generated image [27]. The prerequisite for these methods is that StyleGAN can stably generate images. But in practical experiments, we observed that when the dataset is not large enough, the large image span and complex terrestrial objects in remote sensing images make it difficult for StyleGAN-v2 to stably generate remote sensing images. These methods are not suitable for remote sensing images. Although existing image translation networks based on StyleGAN-v2 cannot be directly applied to remote sensing image super-resolution reconstruction, their network structure, which can generate less noisy, smooth, high-quality images, still holds potential.