## 2 The Principle and Basic Network of This Paper
### Parallel Connection of Convolution and Swin Transformer Block (PCCSTB)  
As shown in Figure 3, this block adopts a parallel structure to Swin Transformer and the FEB (Feature-Enhanced Block). Swin Transformer [10] can effectively extract global features and model pixel relationships within different windows. However, in the FEB, the inductive bias of CNNs (translation invariance and locality) remains essential in the process of high-resolution image reconstruction. It can fuse local features, such as image edges, to complement Swin Transformer with locality and enable local information fusion. Therefore, an independent convolutional operation block, called the Feature Enhancement Block (FEB), is added when computing self-attention. For the Swin Transformer branch, this paper directly introduces the Swin Transformer Block, which uses the window-shifted attention mechanism to compute the horizontal and vertical window attention of different heads in parallel, thereby expanding the attention area and integrating cross-window feature information. The first Swin Transformer Layer Block (STLB) adopts W-MSA (Window-based Multi-headed Self-Attention), which is a windowed multi-headed self-attention mechanism that reduces computational complexity compared to the traditional global attention mechanism. The second STLB uses SW-MSA (Shifted Window-based Multi-headed Self-Attention). Since W-MSA can only focus on the content within the window and cannot connect across windows, it leads to an inability to transfer feature information between windows. SW-MSA overcomes this limitation by introducing shifted windows, which allows for the retention of window-based features while also capturing global features and improving computational efficiency, thus achieving a lightweight model. A Multi-Layer Perceptron (MLP), consisting of two fully connected layers and a GELU activation function, is used for further feature transformation. Layer Normalization (LN) is applied before both MSA and MLP, and residual connections are introduced in both modules. Finally, the feature information extracted by the lightweight Transformer layer is obtained. Figure 2: Shallow feature extraction block. In Figure 3, the FEB (Feature-Enhanced Block) branch uses four different colors to represent the features in the original image. The input features first undergo a pixel-shuffle operation for 2\(\times\) up-sampling, resulting in a high-resolution image that retains the complete information of the original features. After pixel-shuffle, a nonlinear operation, max-pooling is applied to better extract local features before convolution operations. It operates on different channels of the original image to preserve the main information of the original features, with a window size of 2 \(\times\) 2, ensuring that the input and output features match in terms of their spatial dimensions. Next, group convolution (Group-Conv) is used to ensure that the output features maintain consistency with the input features in terms of channel dimensions. Finally, before entering the depthwise separable convolution, a residual connection is applied to add the extracted image information back to the original features, enhancing the features. To better fuse the feature information from the two branches, and since using multi-scale feature representations is crucial for achieving high-quality image super-resolution, a fusion block is designed, as shown in Figure 4. [PERSON] [16] and [PERSON] [17] proposed extracting multi-scale features by using convolutional kernels of different sizes. In this paper, a 3 \(\times\) 3 convolution with different dilation factors is used, which has fewer parameters. To compensate for the missing pixels in the receptive field of dilated convolutions, a progressive-addition method is proposed. Specifically, the fused features are divided into multiple levels, with each level using convolutions with different dilation factors [18] to extract multi-scale information.