Nighttime Dehazing

Nighttime dehazing aims to improve the visibility of images captured at night under hazy conditions, addressing challenges like low light, glow from artificial sources, and scattering effects that obscure details. Current research focuses on deep learning models, often employing transformer architectures or masked autoencoders, to address these issues through techniques such as spatial-frequency filtering, self-prior learning, and semi-supervised training strategies. These advancements are significant for improving the performance of computer vision systems in low-light environments and have implications for applications such as autonomous navigation and nighttime surveillance.