# 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
In the test, the CPU platform took 680 ms to complete the single frame of the image, while the FPGA accelerator took only 19 ms. in terms of real time, the CPU platform processed 1.47 frames per second, while the FPGA could achieve a rate of 52 frames per second. Relative to the CPU, our designed accelerator achieved about 35.3-fold performance improvement. In addition, our algorithm exhibits comparable visual processing compared to a previous lightweight low-light image enhancement system [50] using FPGAs. Thus, by employing FPGAs as accelerators, we could leverage their advantages in terms of energy efficiency and real-time performance, resulting in higher computational performance and faster execution times. From the processing results of this study's algorithm, the dark light region could enhance the visual visibility of this part of the image, retain more image detail information, especially in the visual effect of the ground around the car and the house, and meet pedestrians and drivers on their visual information needs, but also effectively solve the low-light image due to headlights and other light sources caused by the glare problem. From the comparison of these visual effects, the algorithm in this paper could better solve the problem of uneven illumination of low-light images due to glare, enhance the visual effect of the darker regions of the image and at the same time suppress the brightness of the image glare regions. In dealing with this kind of image problem it has obvious advantages: focus in the processed image was more prominent, image distortion was lower, and the details of the information were richer. At the same time, the algorithm had lower power consumption and better real-time performance in hardware implementation compared with accelerators on other general-purpose hardware platforms. ## 5 Conclusions

The GS-AGC algorithm proposed in this paper was based on regional brightness perception. Compared to other approaches, such as using complex regional segmentation combined with local detail enhancement algorithms, or foggy night glow removal algorithms with limited glare targeting, as well as deep learning algorithms that are challenging to implement on resource-constrained embedded devices, the algorithms introduced in this paper demonstrated significant effects in suppressing glare and enhancing overall visual quality of low-light images. Importantly, these effects are achieved while preserving the detail information of the images. To address the real-time processing requirements of image processing, the algorithm presented in this study was deployed on a PC and successfully ported to a Xilinx Zynq-7020 development board. This deployment allowed the algorithm to effectively handle glare in real-time scenarios, overcoming the limitations of competing algorithms. Additionally, the algorithm demonstrated low execution time, requiring only 19 ms to process 1280 \(\times\) 720 low-light images, which was sufficient to meet real-time demands. The design approach adopted in this study was straightforward and effectively improved the sense of hierarchy in low-light images, leading to the desired results. Currently, this issue addresses the uneven illumination and glare problem in low-light environments, yet it does not resolve the issue of excessive glare suppression in complex real-world situations, as the number and scope of glares are too extensive, leading to over-suppression of glare during suppression. This problem can be the main research direction in the future, and the unsupervised network-based approach can then be used to achieve glare suppression and low-light enhancement in low-light images. We plan to integrate the photodecomposition induction luminance network and the illumination learning network. In the photodecomposition induction luminance network, we will reduce the glare range by combining glare detection and brightness suppression using the brightness sensor module.