Aiming at the problem that aerial images are often affected by hazy weather with image blurring and loss of details, an improved SRGAN algorithm is proposed to remove haze in aerial images quickly and efficiently and restore image details and texture information. In this paper, the core structure of discriminator SResblock is redesigned and CBAM attention mechanism is introduced to improve the original SRGAN, and DH-SRGAN algorithm is proposed. The test results on the VISDRONE outdoor aerial synthetic fog dataset show that the proposed algorithm achieves significant improvement in the fog removal of a single image, with the defogged image reaching 24.48 dB PSNR and 95.29% SSIM compared to the original image, which are better than the traditional algorithms in both metrics. Compared with original SRGAN, the DH-SRGAN algorithm is more lightweight and suitable for embedding into the image preprocessing process of UAV reconnaissance missions.