## 4 Experiments and Discussion
### Real Image Experimental Results and Discussion
#### 4.2.2 Accuracy of Target Retention  
The purpose of nonuniform background elimination is to better ensure subsequent target recognition. Therefore, the algorithm should preserve targets in surveillance images as much as possible. To see the result more intuitively, a nonuniform background requiring elimination is shown in Figure 12, where (a-c) are the original surveillance images and (d-o) refer to the nonuniform background that needs to be estimated and eliminated by different algorithms.  
To gain a better idea of how accurate different algorithms are at retaining their targets, we determined the number of targets that are retained after background elimination based on a connected domain method. The target retention results are shown in Table 3.  
In the THT method, as explained in Section 4.2.1, the accuracy of nonuniform background elimination is relatively low. This means that some nonuniform background noise remains in the processed image, significantly reducing target detection accuracy. In the MIF method (5 iterations), the target gray value is decreased to some extent after several iterations, resulting in the loss of low-SNR targets. Moreover, certain brighter targets are mistaken for the nonuniform background noise that is to be deleted. The INTHT method is limited by the fixed-size structural operator, leading to some targets being mistaken for the background and lost. Hence, its target retention accuracy still needs to be improved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Figure 11a**} & \multicolumn{2}{c}{**Figure 11b**} & \multicolumn{2}{c}{**Figure 11c**} \\ \hline
**Background** & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline THT & 21.2518 & 9.3417 & 21.7236 & 9.1999 & 21.8202 & 9.3119 \\ MIF & 3.1559 & 3.0220 & 2.1904 & 2.4297 & 2.4088 & 2.5845 \\ INTHT & 0.4408 & 0.4983 & 0.4270 & 0.4077 & 0.5071 & 0.5887 \\ IBSGM & 0.0231 & 0.1502 & 0.0249 & 0.1559 & 0.0245 & 0.1736 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Means and standard deviations of the residual image obtained by different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Figure 12a** & **Figure 12b** & **Figure 12c** \\ \hline THT & 83\% & 85\% & 81\% \\ MIF & 86\% & 88\% & 85\% \\ INTHT & 93\% & 96\% & 92\% \\ IBSGM & 98\% & 99\% & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of target retention rates using different methods.  
Meanwhile, with the IBSGM method, two structural operators are constructed with a size that constantly self-adapts to the target size and combined with morphological operations. This eliminates nonuniform backgrounds with high precision and robustness while retaining more targets. In some instances, the rate of target retention cannot reach 100% due to interference from too many targets in the surveillance image.  
Figure 12: Nonuniform background estimation by different algorithms. (**a–c**) Real original surveillance images. Nonuniform backgrounds estimated by (**d–f**) THT; (**g–i**) MIF; (**j–l**) INTHT; and (**m–o**) IBSGM.