## 2 Related Work  
### Image Super-Resolution Reconstruction Based on Deep Learning  
The pioneering advancements in image super-resolution reconstruction were spearheaded by [PERSON] et al. [21]. They introduced the super-resolution convolutional neural network (SRCNN), a model that reconstructed high-resolution images by conducting feature extraction and non-linear mapping of input images. Building upon this foundation, [PERSON] et al. [22] expanded the SRCNN model by integrating deconvolution and shared mapping layers, altering the original feature dimension, thus creating the fast super-resolution convolutional neural networks (FSRCNNs). This modification notably accelerated network training. With the evolution of convolutional neural network architectures, [PERSON] et al. [23] introduced the residual network and perceived loss, unveiling the very deep convolutional networks (VDSRs). [PERSON] et al. [24] introduced the deep recursive residual network (DRRN), implementing a multi-path model that comprised local residual learning, global residual learning, and multi-weight recursive learning. This construction led to a deeper network structure that significantly enhanced the super-resolution reconstruction effect. [PERSON] et al. [25] introduced the super-resolution generative adversarial network (SRGAN) and incorporated a generated countermeasure network into the super-resolution reconstruction process. [PERSON] et al. [26] introduced residual dense blocks, eliminating the batch normalization layer, thereby proposing enhanced super-resolution generative adversarial networks (ESRGANs). [PERSON] et al. [27] introduced the enhanced deep residual networks (EDSRs), removing the BN layer from SRResNet and utilizing the saved space to augment the model's depth. [PERSON] et al. [28] designed the residual dense network (RDN), incorporating dense connection blocks based on SRDenseNet and ResNet. Furthermore, [PERSON] et al. [31] proposed the residual channel attention networks (RCANs), integrating a channel attention mechanism to dynamically readjust channel characteristics, thereby achieving enhanced accuracy and visual effects. Additionally, [PERSON] et al. [30] devised a novel method for multi-frame, super-resolution image reconstruction. Their approach focused on increasing visual information and enhancing automatic machine perception to bolster the effectiveness of multi-frame, super-resolution image reconstruction.  
The transformer has recently garnered attention within the computer vision community due to its significant success in natural language processing [46]. A range of transformer-based methodologies have emerged, excelling in advanced visual tasks and showcasing strengths in modeling long-range dependencies [47; 48]. Capitalizing on its impressive performance, the vision transformer was also adapted for low-level visual tasks. For instance, the pre-trained image-processing transformer (IPT) introduced a ViT-style network and implemented multitasking pre-training specifically for image processing [49]. Likewise, the image restoration using Swin Transformer (SwinIR) proposed an image restoration converter based on a hierarchical vision transformer using shifted windows [50; 51]. The encoder-decoder-based transformer (EDT) furthered the exploration by adopting a self-attention mechanism and a multi-task pre-training strategy, thereby advancing research in stochastic resonance [52]. Additionally, the hybrid attention transformer (HAT) devised by [PERSON] et al. [53] amalgamated channel attention and self-attention, establishing an overlapping cross-attention module to augment cross-window information interaction.  
However, the aforementioned models, aiming to enhance super-resolution reconstruction performance, introduce intricate deep network structures. Consequently, these complexities escalate the computational load during network training and inference, rendering them unsuitable for deployment in environments constrained by limited computing resources.