Glare Reduction

Glare reduction research focuses on mitigating the negative impact of bright light sources on image-based systems, particularly in autonomous vehicles and environmental monitoring. Current efforts involve developing and evaluating various glare reduction techniques, including sophisticated image processing algorithms and machine learning models like neural networks (e.g., Multilayer Perceptrons, Recurrent Neural Networks) and adaptations of existing algorithms (e.g., LexRank). These advancements aim to improve the accuracy and reliability of computer vision tasks, such as object detection and classification, across diverse applications, ultimately enhancing safety and efficiency in areas like autonomous driving and wildlife surveys.