## 1 Introduction  
Remote sensing images serve as a crucial data source, offering rich texture details essential for various fields, like change detection [1], target recognition [2], and land cover classification [3]. Their significance in the transportation industry cannot be overstated since these images provide indispensable insights for planning, design, construction, and monitoring of infrastructure [4; 5; 6; 7]. Remote sensing images provide essential ground information crucial for various applications such as infrastructure planning, traffic flow analysis, construction progress tracking, facility status monitoring, environmental impact assessment, and rapid response to disasters [8; 9]. The accuracy of data obtained from these images is fundamental, offering a scientific basis and critical input for decision-makers. However, several factors can compromise the quality of remote sensing images [10]. These factors range from mechanical errors in the optical imaging system and relative motion between the sensing platform and the ground to atmospheric disturbances, noise, and other variables [11]. While improving the resolution of remote sensing images through hardware upgrades is an option, it is often time-consuming and expensive [12; 13]. Nevertheless, the development of integrated positioning, navigation, and time service systems (PNT systems) has catalyzed research into new remote sensing algorithms, particularly for mobile terminals and edge devices [14]. Consequently, specialized algorithms have become essential for the super-resolution reconstruction of low-quality and low-resolution remote sensing images, enabling the acquisition of high-quality visuals. The traditional super-resolution reconstruction methods encompass interpolation [15], reconstruction [16], and learning-based [17] techniques. Interpolation primarily operates on known grayscale information within low-resolution images. Utilizing interpolation formulas, it enhances grayscale data between pixels to achieve image magnification. The interpolation methods typically include linear and nonlinear interpolation algorithms, such as nearest neighbor interpolation, bicubic interpolation, and wavelet transform interpolation. Reconstruction methods involve utilizing multiple low-resolution images alongside unknown high-resolution images to extract image feature information and are typically categorized into frequency domain and spatial domain techniques. Notable algorithms within this realm include iterative back projection, maximum a posteriori probability, and MAP/POCS methods [18; 19; 20]. Learning-based methods acquire prior knowledge to guide image reconstruction by learning the mapping relationship between low-resolution and high-resolution images. These methods commonly encompass flow learning and sparse coding. Although these traditional techniques have facilitated the progress of remote sensing image reconstruction, they heavily rely on constraint construction and image alignment accuracy to achieve reconstruction effects and tend to falter in super-resolution reconstruction tasks that involve significant magnification. As a result, the outcomes often manifest issues such as blurred edge textures and inadequate detailing. In recent years, artificial intelligence techniques leveraging deep learning have seen extensive application in enhancing and rectifying both structured and unstructured data due to their proficiency in artificial feature extraction and screening. Within the domain of remote sensing images, numerous researchers have proposed super-resolution reconstruction models based on convolutional neural networks (CNNs) [21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Despite their effectiveness in enhancing reconstruction performance, these super-resolution models introduce additional computational complexity, resulting in substantial computing costs and increased memory consumption.