Haze Removal

Haze removal aims to computationally restore clear images from those degraded by atmospheric scattering, improving visibility in various applications. Current research focuses on developing sophisticated deep learning models, including transformers and convolutional neural networks, often incorporating techniques like color correction, adaptive filtering, and multi-stage processing to address color distortion and enhance detail. These advancements are improving the accuracy and efficiency of haze removal, impacting fields such as remote sensing, autonomous driving, and underwater imaging by enhancing image quality and enabling more reliable analysis. Furthermore, some research explores the integration of physical models with deep learning approaches for more robust and accurate results.