# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 3 Experiments
### Experimental Design
#### 3.2.1 Validation of Effectiveness  
To validate the effectiveness of the proposed algorithm for removing stripe noise from side-scan sonar images, experiments were performed on images with added virtual stripe noise. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were used as evaluation metrics to assess the effectiveness of stripe noise removal. As the proposed method does not involve deep learning and deep learning approaches for stripe noise removal typically require extensive training datasets, which incurs significant time costs, the proposed method was compared with the following commonly used noise removal methods:1. Guided filter (GF) [13];
2. Wavelet decomposition for stripe noise removal [11];
3. Fourier transform for stripe noise removal [14];
4. Median filtering [10].  
Experiments were conducted using a side-scan sonar image without stripe noise, as shown in Figure 4.  
First, virtual stripe noise was added to the selected image. Then, denoising was applied, using five different methods for a comparative analysis. The denoising results are shown in Table 1.  
Through experiments on denoising virtual stripe noise, we found that the commonly used denoising methods, such as GF denoising, wavelet decomposition, and median filtering, do not effectively remove stripe noise in side-scan sonar images. In contrast, Fourier transform denoising and the method proposed in this paper show superior performance.  
In Table 1, it can be seen that Fourier transform denoising shows good results, but residual shadows of the original noise can still be observed after denoising. In contrast, our proposed method achieves better noise removal, resulting in clearer images with smoother textures.  
Table 2 lists the PSNR and SSIM values of the five methods used to denoise images with virtual fringe noise. As can be seen in the table, the proposed method is significantly superior to the other methods in terms of the PSNR and SSIM values, and it also has  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Added Analog** & **GF** & **Wavelet** & **Median Filter** & **Fourier** & **Proposed** \\
**Image** & **Fringe Noise** & **GC** & **Decomposition** & **Median Filter** & **Transform** & **Method** \\ \hline
1 & & & & & & \\
2 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denoising effect on images with virtual fringe noise.  
Figure 4: Side-scan sonar image without fringe noise. (**a**) Image 1. (**b**) Image 2.  
obvious advantages in terms of the peak signal-to-noise ratio and image structure when processing images with added analog noise. Although the Fourier transform denoising method achieves good visual results, they are not reflected in the evaluation index. Wavelet decomposition and median filtering are numerically superior to Fourier transform, indicating that they have better effects when removing other types of noise, but they clearly have great shortcomings in terms of visual results. These results show that the proposed method not only performs well in the objective evaluation index in removing the fringe noise of side-scan sonar images but also performs consistently in the subjective evaluation of the visual results.  
In general, an analysis of the experimental results shows that the proposed method has significant advantages in the denoising task of virtual fringe noise, as it can effectively remove noise and maintain the clarity and texture characteristics of the image and, to a certain extent, it may achieve better results when combined with other traditional denoising methods.