## 1 Introduction  
Images are often affected by various factors such as hardware and software during the acquisition process, leading to degradation and blurring, which significantly impacts subsequent image processing tasks. Single-image super-resolution (SISR) is a widely studied computer vision task, with the goal of performing super-resolution reconstruction on a single low-resolution image to obtain a high-resolution image. This technology is widely applied across various fields and holds significant research importance. So far, deep learning-based methods have made outstanding contributions to the field of super-resolution (SR). [PERSON] et al. [1] were among the first to propose a convolutional neural network-based super-resolution network, which used only three convolutions to learn the end-to-end mapping from low-resolution to high-resolution images. However, the limited number of network layers resulted in restricted performance. Inspired by the deep residual network ResNet [2], [PERSON] [3], [PERSON] [4], and others stacked residual blocks to extract image features and perform upsampling for high-resolution reconstruction, which further improved the performance of super-resolution networks. The Enhanced Deep Super-Resolution Network (EDSR) further improved performance by removing batch normalization (BN) layers that were unsuitable for SR tasks. In RCAN, [PERSON] et al. [5] proposed a deep residual network based on the RIR structure and channel attention (CA) mechanism, which achieved good results at the time. However, these models are essentially CNN-based, where the size of the convolutional kernels limits the spatial range of detection, making it difficult to capture long-distance dependencies. This means that only shallow features can be extracted, and deep and global features cannot be captured, which hinders high-resolution image reconstruction. In recent years, breakthroughs in Transformer in natural language processing have sparked great interest in applying it to the field of computer vision. The key idea behind Transformer is \"self-attention\", which can capture long-range dependencies between sequence elements, and it has been successfully applied to tasks such as image recognition [6], object detection [7], and image segmentation [8]. [PERSON] et al. [9] introduced the local mechanism of CNN into Vision Transformer, proposing an enhanced network called Local ViT (Local Vision Transformer) that combines both local and non-local information. [PERSON] et al. [10] proposed Swin Transformer, which incorporates the moving window mechanism into Transformer. The window-shifting operation includes both non-overlapping Local-windows and overlapping cross-windows. By limiting attention calculation within a window, the computation load is significantly reduced, and the window-shifting operation allows the attention mechanism to capture global features effectively. [PERSON] et al. [11] proposed a powerful image restoration network, SwinIR (Image Restoration using Swin Transformer), based on Swin Transformer. This network utilizes multiple Swin Transformer layers for local attention and cross-window interaction, while also incorporating convolutional layers for feature enhancement. Through the combination of Transformer and CNN, SwinIR outperforms other state-of-the-art super-resolution methods. Although these methods achieve excellent visual effects and high performance metrics, they require a large amount of training data and GPU memory, which makes them unsuitable for practical application requirements. To address the aforementioned issues, this paper proposes a single-image super-resolution network based on PCCSTB. The module specifically consists of a Swin Transformer module and an FEB in parallel, and feature fusion between the two parallel branches is achieved through the FB. The proposed network extracts and aggregates both local and global features of the image by gradually expanding the receptive field. The network includes three key modules: PCCSTB, SFEB, and FB.