Low Light Condition

Low-light condition research focuses on improving image and video quality, and enabling robust computer vision tasks in dimly lit environments. Current efforts concentrate on developing novel deep learning models, often employing convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs), to enhance image brightness, reduce noise, and restore detail. These advancements are crucial for applications like autonomous driving, surveillance, and robotics, where reliable perception in low-light is essential for safety and functionality. The field is also seeing increased use of multi-modal approaches, combining data from visible light and thermal cameras to improve reconstruction and object detection.