# 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
The larger the value of VIF, the higher the clarity and the better the quality of the image. The smaller the value of MSE, the better the quality of the image and the better the model of the network. Tables 1 and 2 present quantitative results for low-light data, comparing our proposed GS-AGC algorithm with both deep-learning methods and classical low-light image enhancement methods. The results indicate that the GS-AGC algorithm exhibited certain advantages to some extent. On the BDD dataset, the above comparison shows that although our GS-AGC was 0.25 lower than AGC on the information entropy metric, it was slightly better than the AGC \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **PSNR** & **Entropy** & **VIF** & **MSE** \\ \hline Jin Y & 27.84 & 7.19 & 0.80 & 106.69 \\ HWMNet & 27.88 & 6.85 & 0.80 & 105.84 \\ Zero-DCE & 27.20 & 7.20 & 0.79 & 98.27 \\ AGC & 27.76 & **7.62** & 0.80 & 108.87 \\ Dark channel prior & 26.33 & 6.33 & 0.71 & 97.03 \\ Histogram equilibrium & 28.13 & 7.16 & 0.80 & 93.17 \\ Ours & **28.91** & 7.37 & **0.81** & **83.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of different algorithms on the BDD dataset, and bold is the best value. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **PSNR** & **Entropy** & **VIF** & **MSE** \\ \hline Jin Y & 27.43 & 7.29 & 0.80 & 117.25 \\ HWMNet & 27.87 & 6.17 & 0.68 & 106.07 \\ Zero-DCE & 27.58 & 6.91 & 0.81 & 113.49 \\ AGC & 27.41 & 6.72 & 0.80 & 118.05 \\ Dark channel prior & 27.61 & 6.58 & 0.79 & 118.55 \\ Histogram equilibrium [22] & 27.82 & 6.55 & 0.81 & 107.32 \\ Ours & **28.10** & **7.74** & **0.81** & **102.59** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of different algorithms on LLVIP dataset, and bold is the best value. algorithm on all other metrics. For example, in the PSNR metric, we were 1.15 higher than AGC; in the VIF metric, we were 0.01 higher than AGC; in the MSE metric, we were 25.47 better than AGC. Compared to other algorithms, our algorithm had the highest scores in PSNR, information entropy, VIF, and MSE evaluation metrics. On the LLVIP dataset, our GS-AGC algorithm had the same score as histogram equalization and Zero-DCE on the VIF metric with 0.81, but we had the best results on the remaining PSNR, information entropy, and MSE metrics. Compared to the remaining Jin Y, HMMNet, AGCgamma, and dark channel prior, we had the best scores in the above four evaluation metrics, which indicated that the low-light images enhanced in this paper had high visual quality. Figure 8 displays the visual effect graphs of the experimental subjects, showcasing the outcomes achieved by employing various processing methods. From the figure, it is evident that certain image enhancement algorithms like the AGC algorithm, Zero-DCE algorithm, the histogram equalization algorithm, and HMMNet tended to excessively enhance local regions in low-illumination datasets with light sources. On the other hand, although the dark channel a priori algorithm effectively suppressed glare in images, it brought down the overall brightness, resulting in unsatisfactory visual effects. This observation indicates that relying solely on a single image enhancement method has limitations in terms of improving both glare suppression and image brightness balance. In practical experiments, our algorithm was comparable to [PERSON]'s algorithm on the LLVIP dataset and had better visual effects near the dark light region on the BDD dataset, while the shape of the light source maintained good consistency. However, [PERSON]'s method showed significant color bias near houses, resulting in a certain degree of image distortion. It is worth noting that our algorithm performed better in glare suppression for the presence of glare in the real low-light image dataset and was comparable to other methods in terms of enhancement effect.