# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 4 Discussion  
To verify the effectiveness of the proposed algorithm in removing stripe noise from side-scan sonar images, experiments were conducted using images with added virtual stripe noise. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Noisy Image** & **Fourier Transform** & **Proposed Method** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Denoising effect on images with real fringe noise. Figure 6: Side-scan sonar images with real fringe noise. (a) Image a. (b) Image b. (c) Image c.  
were employed as evaluation metrics for denoising performance. The results demonstrate that the proposed method outperforms several common denoising techniques, including guided filter (GF), wavelet decomposition, Fourier transform, and median filtering, in removing stripe noise. Experimental results on side-scan sonar images with added virtual stripe noise reveal that traditional methods such as GF, wavelet decomposition, and median filtering are ineffective in removing stripe noise. Fourier transform denoising and the proposed method show significantly better performance. However, Fourier transform denoising leaves noticeable residual shadows of the original noise, even though it provides better visual results. In contrast, the proposed method offers not only clearer images but also smoother textures, indicating its superior effectiveness in removing noise. The PSNR and SSIM values in Table 2 show that, while Fourier transform denoising produces good visual results, it does not demonstrate the best performance in terms of the evaluation metrics. The proposed method not only demonstrates superior visual results but also excels in terms of the PSNR and SSIM metrics, demonstrating its effectiveness in removing stripe noise from side-scan sonar images. Ablation experiments were conducted to validate the impact of each strategy on the model's performance. By conducting controlled experiments on data terms, priority functions, and matching criteria using the PSNR and SSIM as evaluation metrics on images with added virtual stripe noise, it was found that individually improving the data terms and priority functions led to modest improvements in both metrics. Simultaneous improvements to both resulted in a greater enhancement. Improving the matching criteria alone also improved the PSNR and SSIM metrics, indicating their role in enhancing model performance. The most significant performance improvement was observed in Group 6, confirming the effectiveness of the proposed method. Although the numerical improvements in the PSNR and SSIM were modest (1.838 and 0.014, respectively), a visual analysis showed that the improved model left the least traces of artifacts in denoised images, with superior texture and edge handling effects, validating the effectiveness of the proposed method. Considering the aim of suppressing stripe noise in side-scan sonar images, experiments were also conducted on images containing real stripe noise for validation and comparison with Fourier transform denoising. The results indicate that the proposed method effectively removes stripe noise of a certain width and performs well in texture restoration. However, it exhibits some disadvantages in removing narrow or subtle stripe noise compared with the Fourier transform, which excels in smoothing such noise. Therefore, a combined approach using the proposed method to remove wider stripe noise followed by Fourier transform for smoothing could yield optimal results. In summary, the experimental analysis demonstrated that the proposed method has significant advantages in removing stripe noise from side-scan sonar images, effectively eliminating noise while preserving image clarity and texture features. The ablation experiments underscored the importance of each component in enhancing model performance. Despite modest numerical improvements, the visual results validated the method's effectiveness.