An Aerial Image Dehazing Algorithm Using a Prior-Based Dense Attentive Network

Abstract

To address the problem that the acquired images tend to degrade in clarity and fidelity in aerial hazy conditions to the extent that the target is difficult to detect, this paper proposes an aerial image dehazing algorithm using a prior-based dense attentive network. The network is based on dense blocks and attention blocks with an encoder-decoder structure, which can directly learn the mapping between the input image and the corresponding haze-free image without relying on the traditional atmospheric scattering model. In addition, to better handle inhomogeneous hazy images, the initial fuzzy density map is first extracted from the original hazy images and then used as a common input to the network together with the original hazy images. Finally, this paper synthesizes a large -scale aerial image dehazing dataset containing two subsets of uniform and non -uniform images. The experimental results and data analysis show that the proposed method exhibits better performance of dehazing with other algorithms on both synthetic and real images.