Hazy Image

Hazy images, characterized by reduced visibility and contrast due to atmospheric scattering, pose a significant challenge for computer vision applications. Current research focuses on developing effective dehazing techniques, primarily employing deep learning models such as convolutional neural networks, diffusion models, and generative adversarial networks, often incorporating multi-scale processing and attention mechanisms to improve detail preservation and generalization to real-world scenarios. These advancements are crucial for improving the performance of various systems reliant on clear imagery, including autonomous vehicles, drone surveillance, and intelligent imaging systems operating in adverse weather conditions. The development of large-scale, realistic datasets and novel training strategies, such as contrastive learning and domain adaptation, are also key areas of ongoing investigation.