## 4 Experiments
### Ablation Study
#### 4.4.2 Ablation of Network Modules  
This section investigates the effects associated with each unit on the deraining accomplishment of the network through ablation experiments. The experiments were conducted through instruction on the synthetic collection Rain1200 and evaluation the PSNR and SSIM values on its test set, as well as training on the real dataset RealRain and testing the NIQE and PIQE values on its test set. The effectiveness of the network modules was demonstrated through these evaluations. Table 2 displays the relevant ablation findings.  
To confirm the wavelet transform's function, we removed the wavelet transform module from the network. The approximate rain streak map generated by RSENet and the input rainy image were passed directly through the IMARM, U-Former, and DRCSNet subnetworks, while keeping other conditions constant during training and testing. As shown in Table 2, removing the wavelet transform resulted in a decrease of 1.51 dB in PSNR and 0.010 in SSIM, compared to the proposed MSWT-SSIDA algorithm. This indicates that wavelet transform is crucial for better localizing the rain streaks in rainy images and improving the overall network performance in deraining.  
Similarly, to confirm the efficacy and advantages of the RSENet module for rain streak extraction, the IMARM for feature fusion and reconstruction, and the U-Former module for enhancing feature extraction, experiments were conducted by sequentially removing each module while maintaining other conditions. The outcomes of the experiment demonstrate that after removing the RSENet module, the PSNR and SSIM values decreased by 0.35 dB and 0.003, correspondingly, demonstrating that the RSENet module significantly enhances the network's deraining capability. Likewise, after removing the IMARM, the PSNR and SSIM values decreased by 0.35 dB and 0.003, correspondingly, proving that IMARM also improves deraining performance. In the ablation experiment where the U-Former subnetwork module was removed and replaced by the original UNet \(3\times 3\) convolutional layers, with other conditions unchanged, the results showed that the network using the original U-Net \(3\times 3\) convolutional layers had a decrease of 0.26 dB in PSNR and 0.008 in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Input** & **MSWT-DN** & **MSWT-SSIDA** \\ \hline NIQE & 13.72 & 12.83 & **11.55** \\ PIQE & 10.65 & 9.93 & **9.16** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiments on different branches. (The bold words indicate the experimental results of the algorithm proposed in this paper.)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Algorithms** & \multicolumn{2}{c}{**Rain1200**} & \multicolumn{2}{c}{**Rain1400**} \\ \cline{2-5}  & **PSNR** & **SSIM** & **NIQE** & **PIQE** \\ \hline Removal of wavelet transform & 31.31 & 0.906 & 13.58 & 10.34 \\ Removal of RSENet & 32.47 & 0.913 & 13.65 & 10.11 \\ Removal of IMARM & 31.89 & 0.904 & 13.93 & 10.26 \\ Removal of U-Former & 31.96 & 0.911 & 13.89 & 10.18 \\ Removal of channel space attention & 29.62 & 0.876 & 14.06 & 10.54 \\
**MSWT-SSIDA** & **34.86** & **0.961** & **11.52** & **9.13** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments on individual network modules. (The bold words indicate the experimental results of the algorithm proposed in this paper.)SSIM, compared to the network using the proposed U-Former module. This indicates that the proposed U-Former module performs better in the deraining task.  
Finally, the ablation results of the network without channel and spatial attention mechanisms are also presented in Table 2. In the deraining task on the Rain1200 dataset, the proposed MSWT-SSIDA algorithm achieved a 5.24 dB improvement in PSNR and a 0.085 increase in SSIM, reaching 0.961, relative to the network without channel and spatial attention mechanisms. This further demonstrates that the introduction of channel and spatial awareness mechanisms can effectively improve the network's deraining capability.