Low Light Vision

Low-light vision research aims to improve the performance of computer vision systems in dimly lit conditions, a significant challenge due to reduced image quality and detail. Current efforts focus on developing deep learning models, including generative adversarial networks (GANs) and vision transformers (ViTs), often incorporating multi-scale feature extraction and hierarchical architectures to enhance image representations for tasks like object detection and segmentation. These advancements leverage techniques like image fusion (combining visible and infrared data) and bilevel optimization to achieve robust performance across various low-light vision applications, ultimately improving the capabilities of autonomous systems and enhancing safety in low-visibility environments.