Image Dehazing

Image dehazing aims to computationally remove atmospheric haze from images, restoring clarity and detail crucial for various applications like autonomous driving and remote sensing. Current research emphasizes deep learning approaches, employing architectures such as Vision Transformers, U-Nets, and convolutional neural networks often incorporating attention mechanisms and multi-scale feature extraction to handle the complex nature of haze. These advancements focus on improving dehazing accuracy, efficiency (especially for real-time applications), and robustness to diverse haze types and real-world conditions, including the development of new datasets to address the limitations of existing ones. The resulting improvements in image quality have significant implications for numerous fields relying on clear visual data.