Non Homogeneous Dehazing

Non-homogeneous dehazing aims to computationally remove unevenly distributed haze from images, restoring clarity and detail lost to atmospheric scattering. Current research focuses on developing deep learning models, employing architectures like transformers and convolutional neural networks (often incorporating techniques such as deformable convolutions and fast Fourier convolutions), to effectively handle the complex variations in haze density. These advancements are driven by the need for improved image quality in various applications, such as remote sensing and autonomous driving, where accurate scene understanding is crucial. Addressing the limitations of existing datasets through data augmentation and preprocessing techniques is also a significant area of ongoing investigation.