Remote Sensing Image Dehazing

Remote sensing image dehazing aims to restore the clarity of images obscured by atmospheric haze, improving the accuracy of information extraction for various applications. Current research emphasizes developing efficient and effective dehazing models, focusing on architectures like convolutional neural networks (CNNs), transformers, and diffusion models, often incorporating novel modules to enhance feature extraction and long-range dependency modeling. These advancements are crucial for improving the quality of data derived from remote sensing and UAV imagery, impacting fields such as precision agriculture, urban planning, and environmental monitoring. The development of more robust evaluation metrics beyond traditional image quality assessments is also a significant area of focus.