# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 4 Experiments and Discussions
### Accuracy of Nonuniform Background Correction  
In order to analyze the performance of different algorithms, we use the background residuals [29] to evaluate the nonuniform background correction accuracy quantitatively. In background residuals, the mean and standard deviation can represent the amount of residual background noise well. We take the weak star point with the size of 3 \(\times\) 3 pixels as the reference, adjust the structural elements of different algorithms to keep the reference star point with the same energy value, and then compare the performance of the algorithm for nonuniform background elimination through the background residual. The size of S(Bo) is 31/2 pixels, S(Bi) is 29/2 pixels, and S(Bb) is 20/2 pixels in the new top-hat transform [27]. The value of parameter K is 29 pixels, the value of parameter L is 31 pixels, and the value of parameter M is 20 pixels in the INTHT [29] algorithm. At the same time, compare the precision segmentation ability of different algorithms for the space weak target. The smaller the mean and the standard deviation of the corrected surveillance background, the better the performance of the correction algorithm will be. When calculating the background residuals, it is necessary to eliminate the influence of the space targets and stars. We use the method of the excluded domain [29] to eliminate the space targets and stars. In addition, in order to ensure the accurate removal of stars and targets, we will appropriately adjust the threshold with reference to the smallest star point in the surveillance image. In this paper, the smaller the background residuals of the processed image, the better the performance of the algorithm. As shown in Table 2, the minimum background residual indicates the best processing performance of our algorithm. Figure 7 fully shows the background correction results of different algorithms, (a1)-(a3) are different sizes of the original images, which can show more details, and (a4) is a 3D display of (a3), which can show the nonuniformity of the background more intuitively. The target is submerged in background noise and hot pixels, as shown in Figure 7(a4). In Figure 7, (b1)-(b4) are the correction results of the new top-hat transform, (c1)-(c4) are the correction results of the INTHT algorithm, and (d1)-(d4) are the correction results of the proposed ENTHT algorithm. Figure 7(a1-d1) are images with the original size of 6 K \(\times\) 6 K pixels. All algorithms can well correct the nonuniform background of surveillance images, and no obvious difference can be seen in the macro. Figure 7(a2-d2) are 512 \(\times\) 512 pixels. The image background processed by our algorithm is cleaner. This is because our NSE plays a very important role. Therefore, our background residual in Table 2 is the smallest. Figure 7(a3-d3) are 30 \(\times\) 30 pixels. We can see more details. The background noise processing ability of the INTHT algorithm and the new top-hat transform is poor. There are still some residual background noise and hot pixels around the target. This also leads to large background residuals of the whole image, as shown in Table 2, so the improvement in image quality is not obvious. From Figure 7(a4-d4), it can be intuitively seen that due to the introduction of NSE, our algorithm can remove hot pixels and nonuniform background noise well, and there is almost no residual background noise. This shows that the nonuniform background correction of the space surveillance image is accurate, and the precise segmentation of space objects is realized. The new top-hat transform is a classical algorithm of background estimation based on morphological filtering. INTHT is an improved algorithm of the new top-hat transform, which adds an expansion operation step to restore the background size transformation caused by the different sizes of the structural operators in the previous step to ensure that the nonuniform background can be eliminated more thoroughly.