## 2 Related Work
### Super-Resolution Methods  
As the most classical image processing network, convolutional neural networks were first introduced to super-resolution tasks in SRCNN [21]. SRCNN consisted of three convolutional layers, achieving image super resolution in a pre-upsampling and lightweight structure. The input data were upsampled before being sent into the network. However, the three-layer convolutional network lacked sufficient depth to extract deep image features. To address this issue, a very deep convolutional super-resolution network (VDSR) with 20 weight layers was proposed in [17]. The increase in the number of network layers significantly improved the network's ability to extract image textures and super-resolution performance. The residual structure was introduced to super-resolution networks in [22], making it possible to further deepen the network depth. Super-resolution networks generally consist of two parts: feature extraction and image reconstruction. The above method improved the feature extraction performance by increasing the convolutional network depth. To improve the image reconstruction process, a new sub-pixel convolution structure was proposed in [23], replacing the traditional interpolation method. Experiment results evidenced that the sub-pixel convolution improved the super-resolution network performance and speed. However, it was difficult for sub-pixel convolution to be applied to non-integer multiples of super resolution. In [24], a post-upsampling super-resolution network structure was proposed. Most of the current super-resolution methods have adopted this post-upsampling structure.  
Since its introduction, the transformer network has outperformed classical convolutional networks in various computer vision tasks. Compared with convolutional networks, the transformer network has a larger perceptual field and is more suitable for extracting global features of images. In recent years, the transformer has also been used in the field of super resolution. An image processing transformer [25] was proposed for low-level computer vision tasks, including denoising, super resolution, and so on. Based on the Swin Transformer, the SwinIR [26] network was proposed for super resolution. SwinIR uses both convolutional networks and Transformer to extract image features, improving performance while achieving a degree of lightweighting. The recently proposed ESRT [27] uses a combination of convolutional networks and transformers, and has made further efforts in network lightweighting.  
With the advancements in the super resolution of natural images, various methods have been applied to remote sensing images. In [28], SRCNN was utilized for multispectral remote sensing image super resolution, unveiling research on remote sensing image super resolution. In [29], SRCNN and VDSR were applied to the single-frame super resolution of remote sensing images. An intensity hue saturation transform was used on the input image to speed up the network computation while preserving the color information. A local-global combined network (LGCNet) was proposed in [30]. It could learn multilevel representations of remote sensing images, including both local details and global environmental priors with its multifork structure. In [31], a deep distillation recursive network (DDRN) was proposed that contained more linking nodes with the same convolution layers and performed feature distillation and compensation in different stages of the network. Remote sensing image super-resolution methods basically followed the development of natural image super resolution. Recently, various Transformer-based backbone networks have also been applied to the super resolution of remote sensing images [32; 33; 34]. In [35], a transformer-based enhancement network was proposed to enhance the high-dimensional feature representation.