# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 3 Experiments
### Experimental Design
#### 3.2.2 Sensitivity Analysis and Evaluation  
We conducted an experiment to validate the contribution of the different strategies to the performance of our model. Using a controlled variable approach, we conducted sensitivity analyses on data items, priority weight functions, and matching criteria. The evaluation metrics included the PSNR and SSIM, using images with added virtual stripe noise. The image in Figure 3a was used as the experimental object, and six sets of control experiments were designed accordingly. The results are summarized in Table 3. In a comparison of Group 1 with Groups 2 and 3, improvements in the PSNR and SSIM metrics were observed when individual enhancements were made to the data items and priority weight functions. By comparing Group 1 with Group 4, it was found that simultaneous improvements to the data items and priority weight functions resulted in greater enhancements in the PSNR and SSIM metrics than individual enhancements. By comparing Group 1 with Group 5, it was found that improvements made solely to the matching criteria resulted in enhanced PSNR and SSIM metrics, contributing to improvements in model performance. Group 6 was compared with the other groups, and an analysis of the metrics indicated that it exhibited the best performance, demonstrating the effectiveness of our proposed method. These findings show that modifications to data items, priority weight functions, and matching criteria individually contribute to performance improvements in the model. More \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multirow{2}{*}{**GF**} & \multicolumn{2}{c}{**Wavelet Decomposition**} & \multirow{2}{*}{**Median Filter**} & \multirow{2}{*}{\
\begin{tabular}{c} **Fourier Transform** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Proposed Method** \\ \end{tabular} } \\ \cline{1-1} \cline{5-6}  & PSNR & 26.204 & 28.960 & 27.699 & 25.816 & 33.837 \\  & SSIM & 0.748 & 0.941 & 0.888 & 0.892 & 0.967 \\ \hline \multirow{2}{*}{Image 2} & PSNR & 19.178 & 18.795 & 18.558 & 21.992 & 24.819 \\  & SSIM & 0.779 & 0.846 & 0.788 & 0.848 & 0.889 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation index of simulated fringe noise denoising. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Data Item**} & \multicolumn{2}{c}{**Priority Function**} & \multirow{2}{*}{
\begin{tabular}{c} **Matching Criteria** \\ \end{tabular} } & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \hline
1 & — & — & — & 31.999 & 0.953 \\
2 & \(\surd\) & — & — & 32.226 & 0.959 \\
3 & — & \(\surd\) & — & 32.514 & 0.961 \\
4 & \(\surd\) & \(\surd\) & — & 33.268 & 0.964 \\
5 & — & — & \(\surd\) & 33.147 & 0.963 \\
6 & \(\surd\) & \(\surd\) & \(\surd\) & 33.837 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance of different policies. over, simultaneous enhancements to these components yield synergistic improvements, with our proposed method (Group 6) achieving the most effective results, as evidenced by the evaluation metrics. The evaluation index above is used to analyze the model, and although the performance is improved, it is not reflected in the numerical values. The strategy proposed in this paper improves the PSNR and SSIM indices by 1.838 and 0.014, respectively, compared with those of the original model. In a visual effect analysis, the six groups of models are used to denoise Figure 5a, with added virtual fringe noise, and the results are shown in Figure 4. It is found that, after improving the data items and priority function separately, the repair trace of Groups 2 and 3 can still clearly be seen in the image after denoising, but it is reduced compared with that of Group 1. Compared with Group 1, the repair traces of Groups 4 and 5 are significantly reduced, but some repair traces can still be seen in the details.