Nighttime Haze

Nighttime haze removal is a challenging computer vision problem focusing on enhancing the visibility of images captured at night, which are often degraded by low light, glow, scattering, and multiple colored light sources. Current research employs deep learning models, including transformer-based architectures and masked autoencoders, often incorporating semi-supervised learning strategies and novel loss functions to address issues like unrealistic brightness and artifact suppression. These advancements aim to improve image quality for applications such as autonomous navigation and nighttime surveillance, where clear visibility is crucial. The development of large-scale synthetic datasets and benchmark evaluations are also driving progress in this field.