Homogeneous Haze

Homogeneous haze removal, a crucial image processing task, aims to restore clear images from those degraded by uniformly distributed atmospheric particles. Current research heavily focuses on deep learning approaches, employing convolutional neural networks (CNNs), transformers, and diffusion models to learn complex mappings between hazy and clear images, often incorporating physical models of atmospheric scattering for improved accuracy. These advancements are significantly impacting various fields, including remote sensing, autonomous driving, and medical imaging, by enhancing image quality and enabling more reliable analysis of visual data. The development of robust and efficient dehazing algorithms continues to be a key area of investigation, with a growing emphasis on handling non-homogeneous haze and improving computational efficiency.