# GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception
## 3 An Adaptive Glare Suppression ALGORITHM Based on Regional Brightness Perception
### Gs-Agc
Figure 8: \(Cont.\)

#### 4.3.2 Local Image Quality Evaluation

In this paper, a combination of two evaluation metrics, namely saliency-guided local quality evaluation (SG-ESSIM) [44] and global and local variation image quality evaluation (GLV-SIM) [45], was utilized with a full-reference image evaluation algorithm to more accurately assess the effect of image glare suppression. The evaluation results are presented in Table 3. The SG-ESSIM algorithm makes use of visual saliency as the weighting mechanism, emphasizing image regions that are salient to human observers. Subsequently, it is combined with a full-reference image quality evaluation algorithm to obtain a comprehensive evaluation of image quality. Similarly, the GLV-SIM algorithm employs fractional order derivatives to quantify global changes in the image while using gradient modes to measure local changes. These two measurements are then combined to calculate a similarity map between the reference image and the degraded image, resulting in an objective image score. Figure 8: Processing effect of different algorithms on the LLVIP dataset and the BDD dataset. The first two images are from the LLVIP dataset, while the latter two images are from the BDD dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Evaluation** & \multirow{2}{*}{**Jin Y**} & **HWMNet** & **Zero-DCE** & **AGC** & \
\begin{tabular}{c} **Dark Channel** \\ **Prior** \\ \end{tabular} &
\begin{tabular}{c} **Histogram** \\ **Equilibrium** \\ \end{tabular} & \multirow{2}{*}{**Ours**} \\ \hline BDD & GLV-SIM & 0.91 & 0.93 & 0.95 & 0.96 & 0.93 & 0.96 & **0.96** \\  & SG-ESSIM & 0.82 & 0.84 & 0.91 & 0.87 & 0.83 & 0.91 & **0.92** \\ LLVIP & GLV-SIM & 0.96 & 0.95 & 0.94 & **0.97** & 0.91 & 0.95 & 0.96 \\  & SG-ESSIM & 0.89 & 0.88 & 0.88 & 0.90 & 0.87 & 0.88 & **0.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different algorithms on localized assessment metrics, and bold is the best value. In Table 3, the results of comparing different algorithms using various metrics in the local target region are presented. Specifically, on the BDD dataset, our GS-AGC algorithm achieved a score of 0.96 on the GLV-SIM indicator, which was the same as that of the AGC and histogram equilibrium algorithms. However, on the SG-ESSIM indicator, our GS-AGC algorithm obtained the highest score compared to that of JinY, HWMNet, Zero-DCE, and dark channel prior. On the LLVIP dataset, our GS-AGC algorithm achieved the same score as JinY did on the GLV-SIM index. However, on the SG-ESSIM index, our algorithm scored 0.02 higher than JinY did. Although our GLV-SIM score was 0.01 lower than AGC's, our SG-ESSIM score was 0.01 higher than AGC's. Additionally, compared to HWMNet, Zero-DCE, dark channel prior, and histogram equalization algorithms, our algorithm showed the highest scores on the GLV-SIM and SG-ESSIM metrics mentioned above. #### 4.3.3 Road Pedestrian Detection

We used yolov5 [46], a well-known target detection algorithm, to evaluate road pedestrian detection performance. In the process of road pedestrian detection experiment, the coco [47] dataset was used to train the yolov5 model, and the Dark face [48] dataset was used for experimental test. As depicted in Figure 9, our method achieved more visually pleasing results compared to the original image's detection accuracy. Furthermore, our method demonstrated detection accuracy comparable to that of advanced deep-learning enhancement algorithms. This showcases the effectiveness of our approach in achieving accurate road pedestrian detection. For instance, in the first row of the image, the detection accuracy of the image enhanced by our algorithm was 0.23 higher than that of the Zero-DCE algorithm and 0.22 higher than that of the histogram equalization algorithm. However, the JinY algorithm failed to detect the pedestrian riding an electric vehicle in the enhanced image.