A Highway Traffic Image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

To address the problems of low resolution and blurred details in highway images caused by factors such as rain and fog, illumination interference, and nighttime lighting, this paper proposes a highway traffic image enhancement algorithm based on improved GAN in complex weather conditions. The attention mechanism and the multiscale feature fusion were combined to improve the generator network, which could effectively reduce noise while improving the attention of high-frequency region information. The improved PatchGAN in the discriminator used a local discrimination strategy to distinguish the generated image from the real image, and then the Nash equilibrium was achieved through the continuous interaction between the generator and the discriminator, to ensure the integrity and authenticity of the restored image. Compared with other image enhancement algorithms, using PSNR and SSIM as measurement indicators, the experimental results showed that the proposed algorithm's results were, respectively, 21.97% and 12.89% higher in nighttime enhancement, 26.16% and 12.75% higher in rain removal, and 26.56% and 12.1% higher in fog removal. The proposed algorithm can not only retain the image details and feature information, but also produce effective denoising, which increases the reliability of image-based traffic information processing and analysis.