## 1 Introduction
A significant issue is that most research on efficient super-resolution design is carried out on GPU servers, which does not accurately reflect performance on mobile devices [31]. This discrepancy presents considerable challenges in remote sensing image processing, where high resource consumption and slow inferencing speeds are critical concerns [32]. Remote sensing image data typically includes extensive ground feature information, demanding significant computing resources and storage space. This can lead to bottlenecks in large-scale processing of remote sensing images, potentially making the technology economically and practically unfeasible [33]. Delays in accessing and analyzing image data can hinder timely access to vital information, such as crop data for farmers and agricultural scientists [34]. It also impacts planning and decision-making in urban planning and land management, reduces efficiency in environmental monitoring and resource management, and adversely affects resource exploration and development [35]. In critical situations like disaster monitoring and emergency rescue, the swift and accurate acquisition of high-resolution images is crucial for informed decision-making [36]. If the super-resolution reconstruction process entails prolonged inferencing times, it could significantly hamper the efficiency of information acquisition, thereby diminishing the effectiveness of decision support. Hence, to effectively implement the super-resolution reconstruction model in engineering practice, it becomes crucial to explore lightweight network models for achieving rapid and efficient reconstruction of remote sensing images. A lightweight network entails transforming deep learning algorithms to minimize both size and speed while preserving accuracy to the greatest extent possible. Over the past decade, researchers have concentrated on designing lightweight convolutional neural networks, making significant strides in various effective design principles. These include separable convolutions [37], inverted residual bottleneck [38], channel shuffle [39], mixed depth-wise convolution [40], and structural re-parameterization [41]. Consequently, classical models like MobileNets [37], ShuffleNets [39], and RepVGG [41] have emerged. Concurrently, transformer models have gained increasing attention. Transformers, based on the self-attention mechanism, establish a feature extraction network structure capable of calculating attention across each position in the input sequence to derive global context information. Leveraging the self-attention mechanism empowers transformer models to extract contextual feature representations efficiently during training while adapting to input sequences of varying lengths. Furthermore, vision transformers (ViTs) are developed by introducing numerous efficient design principles that notably enhance network computing efficiency on mobile devices. These lightweight ViT models include Efficient Formers [42; 43] and MobileViTs [44; 45], exhibiting superior performance and lower latency compared to CNNs when deployed on mobile devices. To realize the lightweight learning model for the efficient and accurate reconstruction of remote sensing images, a super-resolution reconstruction vision transformer based on structural re-parameterization (SRRepViT) is proposed in this work. The main contributions of our research are as follows:  
* A transformer is used instead of CNN, and the self-attention mechanism of the transformer is used to extract complex spatial and spectral features of remote sensing images. * The neat topology and structural re-parameterization are adopted to reduce model parameters and speed up model inferencing. The paper is organized as follows. Section 2 introduces the background and methodology of image super-resolution reconstruction. Section 3 provides the specific architecture of the network model. Section 4 shows the experimental preparation. Section 5 shows the experimental results and analysis. Finally, Section 6 draws conclusions.