## 1 Introduction  
The interpretation of remote sensing images is one of the important tasks in the field of remote sensing. Whether manual or machine-based, high-quality, high-spatial-resolution image data are essential for detailed interpretation [1]. However, due to the limitations of sensors and data confidentiality, remote sensing image interpreters often do not have access to high-quality image data. And most free datasets have low spatial resolution. For example, the spatial resolution of Landsat data is 30 m [2], Sentinel-2 data of the visible light have a processed resolution of 10 m [3], and Sentinel-1 radar data, after multi-look processing, have a spatial resolution of 20 m [4]. As remote sensing visual tasks increasingly demand higher precision and resolution, traditional low-resolution images can no longer meet the fine-grained requirements. Therefore, super-resolution reconstruction of remote sensing images is considered an effective solution, as it can enhance image resolution and quality. In 1955, [PERSON] first defined the concept of super-resolution in the field of optical imaging clearly. It mainly refers to the process of using optical-related knowledge to recover data information beyond the diffraction limit [5]. In around 1964, [PERSON] and [PERSON] first proposed the concept of image super-resolution, which primarily refers to the process of synthesizing a single-frame image with richer detail information using the method of extrapolating the spectrum [6]. Recently, the methods for single-frame remote sensing image super-resolution reconstruction using deep learning have been mainly divided into two types: one is based on Convolutional Neural Networks (CNNs), and the other is based on GANs. In 2016, [PERSON] applied CNNs to the field of super-resolution. By utilizing sparse coding, multiple overlapping low-resolution image patches are reconstructed into high-resolution patches through the nonlinear mapping of CNNs (SRCNN) [7]. However, due to the shallow depth of the network, the results in SRCNN were not accurate enough. Recently, many studies based on CNNs have proposed effective solutions. [PERSON], [PERSON] et al. proposed Very Deep Super-Resolution (VDSR), which introduced a residual learning method to accelerate the training process and alleviate the vanishing gradient problem [8]. [PERSON], [PERSON] et al. proposed a Residual Dense Network (RDN), which combines residual learning with dense connections, further enhancing the network's ability to recover fine details [9]. [PERSON], [PERSON] et al. introduced Deep Back-Projection Networks (DBPRNs), which use a back-projection mechanism to progressively restore details from low-resolution images [10]. Additionally, Local-Global Combined Networks (LGCNets) [11] and Deep Memory Connected Networks (DMCN) [12] address the issue of insufficient network depth by employing local-global feature fusion and deep memory mechanisms. These approaches, through enhancing network depth or introducing novel architectural designs, have significantly improved super-resolution task performance, making them more powerful for the image reconstruction field. Due to the limited detail recovery and data bias issues in CNNs for super-resolution tasks, their practical effectiveness is constrained. GANs have emerged as a powerful alternative, addressing these challenges through their unique generative capabilities and optimization mechanisms. In 2014, [PERSON] proposed GANs, bringing a new focal point to the field of image generation [13]. In 2018, [PERSON] et al. proposed an hourglass-shaped generative network that accomplished the task of image super-resolution in an unsupervised manner [14]. However, since the network uses noise as the main input, the generated images are easily contaminated by it. Therefore, in 2019, [PERSON] et al. proposed a new generative network to address the issue of noise contamination.