An adaptive defogging method for aerial images on rainy days based on deep learning

Dehazing rainy aerial images poses a challenging problem, as traditional dehazing methods have limited effectiveness under complex lighting conditions and various scenes. Therefore, an adaptive dehazing method for rainy aerial images based on deep learning is proposed. This method utilizes deep learning networks to learn the relationship between haze and foreground information in aerial images and removes the impact of haze by restoring clear images. To adapt to different lighting conditions and scenes, adaptive techniques are introduced in the network design and optimization process to achieve better adaptability and robustness. Experimental results demonstrate that this method has achieved significant improvements in dehazing quality and exhibits strong robustness across various scenes.