## 4 Experiments
### Comparison with Advanced Technology
To address this issue, the proposed method automatically adjusts the image brightness and increases a layer gradient difference through lighting compensation. This leads to a reduction in the missed recognition rate and an improvement in the comprehensiveness of reflection region removal. Figures 8-11 show the reflection removal effect under different lighting conditions. From Figure 10 and Table 2, it can be seen that under lighting conditions, machine learning methods may result in incomplete video reflection removal due to training factors and sudden changes in the environment. The method based on relative motion cannot completely eliminate the reflection area under lighting conditions. The reflection removal methods based on black box videos and static videos have significant errors in selecting ROI regions due to lighting factors. This leads to a blurring of the reflection area and an increase in the false detection rate of obstacles. The method proposed in this paper is effective in removing video reflections. By reducing the weight of lighting brightness and angles, the effect of reflection removal is achieved. The results indicate that under lighting conditions, local reflection areas can be completely eliminated. It reduces the false detection rate of obstacles caused by radiation. Table 3 compares the more popular methods in recent years. It can be seen that the method proposed in this paper has the highest adaptability--that is, the best reflection removal effect--whether in the presence or absence of light. Figure 8: A comparison of five reflection removal methods under a normal driving environment. Figure 9: A comparison of five reflection removal methods under normal lighting conditions. According to Figure 11, it can be seen that the reflection removal effect of each method is relatively better under no light conditions than under light conditions. Due to local reflections, there is a phenomenon of incomplete reflection removal. The reflection removal method based on black box video and still video suppresses the shadow problem that occurs during reflection removal under lighting conditions. The method proposed in this paper can effectively remove local area reflections under weak illumination. Due to the weakening of lighting, the reflection area is not obvious, which has a small impact on vehicle obstacle detection. Experiments have shown that the method proposed in this paper can effectively remove reflections from car mounted videos with and without light, and the effect is good. Figure 10: The results of five reflection removal methods under light conditions where (**a**) is the removal result of the reflection region removal method based on relative motion, (**b**) is the removal result of the black box video reflection removal, (**c**) is the removal result of the reflection region removal method based on image completion, (**d**) is the removal result based on the Static Video method, and (**e**) is the removal result of the reflection region removal method based on the combination of image completion and light compensation. Figure 11: The plot of the results of five reflection removal methods in a normal driving environment where (**a**) is the removal result of the reflection region removal method based on relative motion, (**b**) is the removal result of the black box video reflection removal, (**c**) is the removal result of the reflection region removal method based on image completion, (**d**) is the removal result based on the Static Video method, and (**e**) is the removal result of the reflection region removal method based on the combination of image completion and light compensation.