基于改进SRGAN的无人机航拍图像去雾算法

Improved SRGAN-based algorithm for defogging UAV aerial images

针对航拍图像往往受雾霾天气影响出现图像模糊、细节丢失等问题,本研究提出了一种基于改进SRGAN的无人机航拍图像去雾算法,旨在快速高效地去除航拍图像中的雾霾并恢复图像细节和纹理信息。本文重新设计判别器核心结构SResblock并引入CBAM注意力机制,完成了对原始SRGAN的改进,提出DH-SRGAN算法。在VISDRONE户外航拍合成雾数据集上测试结果显示,本算法在单幅图像去雾方面取得了显著提升,去雾后的图像与原始图像PSNR达24.48dB、SSIM达95.29%,两项指标均优于传统算法。相比原始SRGAN,DH-SRGAN算法更加轻量化,适合嵌入到无人机侦察任务中的图像预处理流程。

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.