Foggy Image

Foggy image processing focuses on developing computational methods to restore clear images from those obscured by fog, a crucial task for applications like autonomous driving and remote sensing. Current research emphasizes the development of robust deep learning models, including transformer-based networks and generative adversarial networks (GANs), often incorporating wavelet transforms to preserve image detail and address challenges posed by non-homogeneous fog and diverse scene types (e.g., overwater). These advancements aim to improve the accuracy and generalization capabilities of defogging algorithms, particularly by leveraging synthetic datasets and self-supervised learning techniques to overcome data limitations. Ultimately, improved foggy image processing will enhance the reliability and safety of computer vision systems operating in challenging weather conditions.