## 3 Experiments and Results Analysis
### Ablation Experiment  
To analyze the actual contribution of various modules in the network, a series of ablation experiments were designed. The network was trained for 106 iterations on input LR images of size 256 \(\times\) 256 pixels from the DIV2K dataset, and evaluated for \(\times\)4 super-resolution using the Urban100 and Manga109 datasets. To ensure fairness and validity in the ablation experiments, and to keep the model size consistent, deeper network layers were added when removing modules with larger parameter sizes. The Feature Extraction (FE) Part of the network was designed with three parts, each containing four, two, and one PCCSTB blocks, with feature channels, attention heads, and MLP expansion factors set to 60, 6, and 2, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{
\begin{tabular}{c} **Sampling** \\ **Scale** \\ \end{tabular} } & \multirow{2}{*}{**Parameters (k)**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\ \cline{3-8}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline HPUN-L & & 1592 & 32.31/0.8963 & 28.75/0.7845 & 27.68/0.7388 & 26.29/0.7919 & 30.78/0.9109 \\ CARFT & & 753 & 32.52/0.8989 & 28.85/0.7872 & 27.72/0.7418 & 26.56/0.7995 & 31.18/0.9168 \\ EDSR & & 1518 & 32.11/0.8940 & 28.58/0.7813 & 27.57/0.7357 & 26.04/0.7849 & 30.35/0.9067 \\ ELAN-light & & 601 & 32.43/0.8975 & 28.78/0.7858 & 27.68/0.7406 & 26.54/0.7982 & 30.92/0.9150 \\ IMDN & \(\times\)4 & 715 & 32.21/0.8948 & 28.58/0.7811 & 27.56/0.7353 & 26.04/0.7838 & 30.45/0.9075 \\ LAPAR-A & & 659 & 32.17/0.8946 & 28.63/0.7820 & 27.63/0.7368 & 26.16/0.7873 & 30.44/0.9076 \\ LatticeNet & & 777 & 32.31/0.8963 & 28.69/0.7831 & 27.63/0.7368 & 26.26/0.7874 & — \\ MAFFSR-L & & 830 & 32.20/0.8953 & 28.62/0.7822 & 27.59/0.7370 & 26.16/0.7878 & — \\ SwinIR-light & & 897 & 32.44/0.8976 & 28.77/0.7858 & 27.69/0.7406 & 26.47/0.7980 & 30.92/0.9151 \\
**PCCSTSR (ours)** & **585** & **32.73**/**0.8998** & **28.89**/**7885** & **27.74**/**0.7432** & **26.63**/**0.8034** & **31.32**/**0.9173** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of image SR reconstruction results (\(\times\)4). Figure 6: Comparison of reconstruction results among original high-resolution image, low-resolution image, and PCCSTSR on the Urban100 dataset (\(\times\)4). To verify the actual effects of SFEB, FEB, ESA, and FB, these modules were individually removed, and experiments were conducted to evaluate their impact on the quantitative metrics PSNR and SSIM. This allowed for the validation of the effectiveness of each module in improving the network's performance. The results of the ablation experiment are shown in Table 4.  
PCCSTSR-SFEB is a network without the SFEB module, designed to validate the role of SFEB in the network. For super-resolution tasks that focus on fine details, shallow feature extraction is critical. The application of this module helps to better extract local information. As shown in Table 3, after removing SFEB, the PSNR values on both datasets decreased by 0.11 dB and 0.08 dB, respectively. PCCSTSR-FEB removes all parts before the 1 \(\times\) 1 and 3 \(\times\) 3 convolutions in the FEB module. Operations such as pixel-shuffle and max-pooling enhance image details. As shown in Table 3, after removing these parts, the PSNR values on both datasets decreased by 0.06 dB and 0.04 dB, respectively. PCCSTSR-ESA is a network without the ESA module. The ESA module is used to enhance spatial attention in deep learning. By introducing a reinforced spatial attention mechanism, the ESA module allows for a larger receptive field and adaptive feature allocation, which helps better capture key information in images. As shown in Table 3, after removing ESA, the PSNR values on both datasets decreased by 0.08 dB and 0.12 dB, respectively. PCCSTSR-FB replaces the FB module in PCCSTB with a normal group convolution with a group size of 4.