## 3 Experiments and Comparison
### Image Correction Effect of Noise  
The previous section's experiment demonstrated that when the number of intercepted image columns is 1500, the effect of the picture after correction becomes more effective and stable. The purpose of this section's experiment is to investigate the effect of noise on image correction. This research adds Gaussian white noise with various variances to the intercepted image. The diagram of the experimental example is presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Images\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslash\textbackslashbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\textbackslashbackslash\We add Gaussian white noise with varying variances to this portion of the image, and then use the Formula (15) to add horizontal stripes to the image. \(g(i)\) conforms to a Gaussian distribution with a mean of 1, and \(b(i)\) conforms to a Gaussian distribution with a mean of 0. \(g(i)\) and \(b(i)\) have the same variances which are shown in the first row of Table 2. The experimental data consist of 20 frames of infrared scan images without nonuniformity; the experimental outcomes are presented in Table 2.  
The image of the infrared column scan is an 8-bit image. The first row of Table 2 is the variance of the added Gaussian noise with a mean of 0 and the variance ranges from 25 to 255. The noise intensity increases as the variance of the Gaussian distribution increases. The second row is the average PSNR of the dataset following correcting. Table 2 demonstrates that when the variance of the added Gaussian noise grows, the PSNR of the image collection swings within a given range, and the connection is not linear. Consequently, the Gaussian noise has little effect on the denoising effect of the algorithm described in this paper.