Low Light Vision Task

Low-light vision research aims to improve computer vision performance in dimly lit conditions, a significant challenge due to reduced image quality and information loss. Current efforts focus on developing novel deep learning models, including generative adversarial networks and those employing hierarchical feature enhancement or self-supervised learning strategies, to improve image restoration and enhance downstream tasks like object detection and segmentation. These advancements leverage techniques such as multi-scale feature fusion, RAW image processing, and domain adaptation to achieve robust performance across various low-light scenarios, impacting applications ranging from autonomous driving to medical imaging. The development of new datasets and benchmark evaluations further drives progress in this crucial area.