## 3 Experiments and Comparison
### Computational Time  
Due to the fact that infrared focal plane linear array scan detectors image tens of thousands of columns per second, the algorithm must have high performance in real time. [PERSON] [16] and other comparison algorithms perform the wavelet transform on the image. The size of a frame for an infrared focal plane array image is limited, and the image's length and width are around a few hundred pixels; thus, the wavelet transform is not a significant burden for it. For the infrared focal plane linear array scan image, the number of columns in a frame is tens of thousands, so the wavelet transforms applied directly to the whole frame of the line column image are not suitable, while the computational overhead is relatively large. In contrast, the proposed algorithm only requires a portion of the infrared  
Figure 10: PSNR results of [PERSON] [16], [PERSON] [17], [PERSON] [18], and our method. In order to verify the universality of the nonuniformity correction effect of the algorithm in this paper, 50 images are selected at random from the FLIRADAS dataset, and Gaussian cross-stripe noise with different variance \(\sigma\) is added as shown in Equation (15). The mean PSNR values of the corrected images are calculated using various algorithms to create a relationship curve, as depicted in this figure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence\textbackslash{}Method** & **Original** & **[PERSON] [16]** & **[PERSON] [17]** & **[PERSON] [18]** & **Proposed Method** \\ \hline bus & **90.00** & 93.19 & 92.56 & 53.86 & **87.97** \\ people & **53.92** & 54.66 & 55.48 & 37.20 & **52.35** \\ building & **21.62** & 23.85 & 24.50 & 15.81 & **20.95** \\ hand & **23.50** & 32.56 & 27.12 & 21.74 & **23.45** \\ stairs & **173.16** & 168.90 & **176.44** & 116.31 & 162.74 \\ dataset & **75.98** & 71.93 & 78.29 & 50.43 & **72.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy of vertical gradient of [PERSON] [16], [PERSON] [17], [PERSON] [18], and our method. scan image to calculate the linear correction coefficients, which are then used to correct the image nonuniformity of the entire frame. The main cost of the algorithm is the computation of the linear correction coefficients across a portion of the image. Therefore, the proposed algorithm is less time-intensive and operates more in real time. To ensure a fair comparison, all techniques are executed in Matlab R2020b without optimizations or parallel-computing implementation on a PC with a 2.50 GHz Intel Core i7-9750 HK processor and 16 GB of RAM. The experimental data in this section are partial images intercepted from real infrared focal plane linear array scan images with an image resolution of 3053 \(\times\) 8192 and an A/D acquisition accuracy of 14-bit. In order to facilitate subjective evaluation, all 16-bit grayscale images are converted to 8-bit data grayscale images. The proposed algorithm in this paper selects the image with the resolution of 3053 \(\times\) 1500 for calculating the linear correction model coefficients. The size of the vertical guided filter window for smoothing the image is 12 \(\times\) 1, and the size of the horizontal guided filter for extracting the horizontal strip is 1 \(\times\) 100. The artificial guided filter parameters \(\varepsilon\) are both 0.16. [PERSON] [16] adopts three-level image wavelet decomposition and haar wavelet. [PERSON] [18] chooses the haar wavelet base and [PERSON]'s [17] algorithm chooses the \"db4\" wavelet. The experimental results are shown in Table 6.  
The algorithm execution time listed in Table 6 is the mean execution time of each algorithm after 10 iterations. An evident conclusion is that, when the image resolution is 3053 \(\times\) 8192, the algorithm of [PERSON] [16] and the algorithm proposed in this paper have similar minimum times of approximately 1s.