# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 4 Experiments and Discussions
### Accuracy of Nonuniform Background Correction
However, these two algorithms have poor performance in processing nonuniform background residual noise. These two algorithms tend to leave isolated small residual noise in the background and are unable to solve the influence of dark current on the image, which will significantly reduce the performance in detecting space targets. Our algorithm can solve this problem well. By utilizing the NSE, our algorithm can properly remove such isolated noise. Compared with the traditional denoising algorithm based on filtering, it will not damage the energy distribution of space targets and can protect the space weak targets while removing the noise. A good algorithm must have a strong anti-interference ability, that is, the stability of the algorithm. During the ground test, we simulated some strong stray light influence \begin{table}
\begin{tabular}{c c c c c} \hline  & **Method** & **New Top-Hat** & **INTHT** &
\begin{tabular}{c} **The Proposed** \\ **ENTHT Method** \\ \end{tabular} \\ \hline Figure 7 & Mean/pixel & 0.5451 & 0.2469 & 0.0521 \\  & Standard Deviation/pixel & 3.9179 & 2.5954 & 1.0123 \\ \hline \end{tabular}
\end{table} 
Table 2: Background residuals of different algorithms. Figure 7: Background correction results of different algorithms. (**a1–a4**) Original surveillance images, (**b1–b4**) Correction results of the new top-hat transform, (**c1–c4**) Correction results of the INTHT algorithm, (**d1–d4**) Correction results of the proposed ENTHT algorithm. with the light source. We took some images affected by different stray lights to verify the stray light suppression performance of our algorithm. From Figures 8(a1-d1) and 9(a1-d1), we can see that the three algorithms can accurately eliminate nonuniform stray light, but from the details, as shown in Figures 8(a2-d2) and 9(a2-d2), our algorithm eliminates the background noise of stray light more accurately, which leads to smaller background residual of the image processed by our algorithm, as shown in Table 3. The experimental results show that even under the influence of strong stray light, our algorithm can still achieve accurate segmentation of weak targets compared with other algorithms, as shown in Figures 8(a3-d3,a4-d4) and 9(a3-d3,a4-d4) while correcting the nonuniform background. The principle of our algorithm is background estimation, so it has a good correction effect for static nonuniform backgrounds and uncertain nonuniform backgrounds. Compared with other scholars' algorithms, due to the introduction of NSE, our algorithm can eliminate noise at a deeper level and eliminate nonuniform backgrounds more accurately, thus achieving accurate target segmentation and improving the imaging quality of surveillance images. Further, compared with the traditional denoising algorithm based on filtering, our NSE will not weaken the weak targets and will not destroy the energy distribution of the targets, which is also an advantage of our algorithm. Figure 8: Background correction results of different algorithms under the influence of stray light. **(a1–a4)** Original surveillance images, **(b1–b4)** Correction results of the new top-hat transform, **(c1–c4)** Correction results of the INTHT algorithm, **(d1–d4)** Correction results of the proposed ENTHT algorithm.