## 3 Experiments and Comparison
### Qualitative Analysis of the Correction Effect of the Algorithms  
On the dataset, the algorithm proposed in this research is compared to the non-uniform correction algorithms [PERSON] (2016), [PERSON] (2017), and [PERSON] (2018) developed in recent years, and their correction effects are evaluated qualitatively. The data collection consists of five infrared images from the FLIRADAS dataset and two frames of real infrared scan images with nonuniformity. Figure 5 depicts the five raw infrared images picked from the FLIRADAS dataset. After adding stripes, the images are depicted in Figure 6. The noise parameter \(g(i)\) in Formula (15) follows a Gaussian distribution with a mean value of 1 and a variance of 0.02, while \(b(i)\) follows a Gaussian distribution with a mean value of 0 and a variance of 0.02. The resolution of the image in Figure 6 is relatively high, and when the image is magnified, the stripes of nonuniformity can be seen. Since the comparative algorithms [PERSON] (2016), [PERSON] (2017), and [PERSON] (2018) all used wavelet transform in the process, different wavelet bases have a substantial impact on the image correction effect. By comparing the effects of each algorithm on different wavelet bases, the one with the best effect is selected for nonuniformity correction, and the results are compared to the algorithm presented in this paper. The common wavelet-based haar wavelet, sym8 wavelet, and db4 wavelet are used for compar \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variance** & 25 & 36 & 49 & 64 & 81 & 100 & 121 & 144 & 169 & 196 & 225 \\ \hline
**PSNR** & 43.90 & 44.02 & 44.14 & 44.12 & 44.17 & 44.12 & 43.79 & 43.93 & 44.03 & 43.55 & 43.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of Gaussian noise in the intercepted image on correction algorithm. Figure 4: Infrared scan image with the addition of Gaussian noise. Due to the very high resolution of the infrared column scan image (3053 \(\times\) 55,000), only a portion of the scanned image (3053 \(\times\) 8192) is displayed in this figure. The image within the red border is the image utilized for the linear fitting model in Section 2.1, with a resolution of 3053 \(\times\) 1500 pixels. correction effects of [PERSON] [16], [PERSON] [17], and [PERSON] [18] with respect to various wavelet bases, are shown in Figure 7.  
Figure 5: Original image (**a1**) stairs (**b1**) people (**c1**) hands (**d1**) bus (**e1**) buildings. Figure 6: Image after adding analog stripes (**a2**) stairs (**b2**) people (**c2**) hands (**d2**) bus (**e2**) buildings. Figure 7: Nonuniformity correction effect of [PERSON] [16], [PERSON] [17], and [PERSON] [18] with respect to distinct wavelet bases. The wavelet bases include “haar”, “db4”, and “sym8” wavelets. The image “bus” is added with stimulated nonuniformity noise. In order to highlight the correction effect of the algorithm on horizontal stripes and the fuzzy degree of the algorithm on image texture details, the images within the red border are enlarged. [PERSON] [17] has a good correction impact on the \"db4\" wavelet, and the texture of the image's details is less blurry. [PERSON] [18] and [PERSON] [16] have severe distortion of image texture detail with the \"db4\" and \"sym8\" wavelets, so they selected the \"haar\" wavelet. The size of the guided filter windows of [PERSON] [16], [PERSON] [17], and [PERSON] [18] are all one-fourth of the width of the processed image sections. Since the simulated image is small, the algorithm in this paper selects the whole frame to calculate the linear correction coefficients without intercepting the image, where the size of the smoothing guide filter window is \(8\times 1\), and the size of the horizontal stripe extraction guide filter window is \(1\times 10\). The guide filter parameters \(\varepsilon\) are all 0.16. Figure 8 depicts the effect of image nonuniformity correction using different algorithms.