A feature attention dehazing network based on U-net and dense connection
Hongyuan Jing, Quanxing Zha, Yiran Fu, Hejun Lv, Aidong Chen · Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021) · 2022
The image acquisition equipment influenced by the dense fog, dust, and other weather factors produced low-contrast and low-quality images, which are unfriendly to smart city applications. Various algorithms, models, and networks have been proposed to achieve the dehazing work, all of which have behaved well to the uniform noise. However, in the real scene, due to the nonhomogeneous distribution of the fog, the performances of the dehazing method still need to be improved. In this paper, we proposed a Feature Attention Dehazing Network based on U-Net and Dense Connection, which is FAD-U-Net, to minimize the influence of the nonhomogeneous haze. The FAD-U-Net employed the U-Net as the base structure used the feature attention model to fit the contracting path of U-Net; in addition, the dense connection technique has been added between each block on both contracting path and expansive path, which aims to enhance the feature information. Finally, a group of 18 residual blocks has been added to the networks to improve the image quality. Experiment results show that the proposed dehazing network outperforms most of the existing dehazing methods under nonhomogeneous fogging environments. Regarding the four state-of-art methods we compared in this paper, the PSNR value is increased by 3.21 on average, and SSIM is increased by 0.14. The FAD-U-Net is suitable for most of the application scene in the smart city.