Sand Dust Image

Sand dust image enhancement aims to improve the quality and visibility of images obscured by sandstorms, a crucial task with applications in atmospheric science and various other fields. Current research focuses on developing advanced deep learning models, including convolutional neural networks (CNNs) and transformer-based architectures, often combined in hybrid approaches to leverage both local and global image features for superior dust removal and color correction. These models strive to overcome limitations of traditional methods by directly addressing the complex scattering effects of sandstorms, leading to more robust and accurate image restoration. The creation of standardized benchmark datasets is also a key area of development, enabling more rigorous and objective evaluation of algorithm performance.