Dehazing Method

Image dehazing aims to computationally remove haze from photographs, improving image quality and visibility. Current research focuses on developing efficient and robust deep learning models, including convolutional neural networks (CNNs) and vision transformers, often incorporating techniques like wavelet transforms, contrastive learning, and attention mechanisms to enhance feature extraction and processing. These advancements are driven by the need for improved performance in various applications, such as autonomous driving, remote sensing, and general image enhancement, where clear images are crucial for reliable system operation. The field is also actively addressing the challenge of generalizing dehazing methods to real-world scenarios, moving beyond reliance on synthetic datasets.