Nighttime Image

Nighttime image processing focuses on overcoming the challenges of low light, poor contrast, and motion blur inherent in night-time scenes to enable accurate computer vision tasks. Current research emphasizes developing robust models, often employing transformer and convolutional neural network architectures, for tasks such as semantic segmentation, depth estimation, and image enhancement, frequently incorporating techniques like domain adaptation and self-supervised learning to address data scarcity and domain shifts between day and night images. These advancements are crucial for improving the performance of autonomous driving systems, surveillance technologies, and other applications reliant on reliable nighttime visual perception. The development of new, photorealistic nighttime datasets is also a significant area of focus, enabling more effective model training and evaluation.