AEE-Net: An Efficient End-to-End Dehazing Network in UAV Imaging System

Abstract

Because it can provide real-time images for the first time, UAV plays a massive role in disaster relief, environmental observation, and information collection. However, the quality of images collected by UAV is always affected by fog. Therefore, the research on how to remove the fog in the image becomes more and more critical. In recent years, the role of convolutional neural networks (CNN), which can automatically extract features and efficiently process high-dimensional data, has received more and more attention in many disciplines. To improve the imaging quality of UAV in a foggy environment, this paper proposes an image dehazing model built with a convolutional neural network (CNN), called an effective end-to-end dehazing Network (AEE-Net). Our proposed method has a faster running speed than traditional models due to the simple structure of the model and the design based on the modified atmospheric scattering model. Our method combines the characteristics of dehazing processes and the advantages of deep learning. Experimental results on the training set and raw images show that the proposed method has better performance than traditional methods. This method can improve the quality of UAV-captured images under foggy conditions and can meet the input requirements of UAV vision tasks.