Aerial images are often applied into disaster area surveillance. High-resolution (HR) aerial images are preferred to monitor the disaster area since they can provide abundant information. However, limited by hardware device and imaging environment, the resolution of captured aerial images may not meet the needs of practical application. Image super-resolution (SR) is an effective way to improve the resolution of captured images in a post-processing manner. Recently, convolutional neural networks (CNNs) have demonstrated great success in image SR. However, these CNN models cannot be easily applied to real-world scenarios due to requiring huge storage and computational resources. To reduce resource consumption, we need to decrease network parameters. Recursive network can effectively reduce network parameters, which motivates us to explore a more effective image SR method. In this paper, we proposed a deep recursive dense network (DRDN) to reconstruct HR aerial images. In the DRDN, the proposed recursive dense block (RDB) can fully extract abundant local features and adaptively fuse different hierarchical features of LR image for HR image reconstruction. In addition, the recursive manner of RDB in DRDN can effectively reduce the parameter of network. The experimental results on aerial images demonstrate the superiority of our proposed method.

航拍图像通常用于灾区监视。首选高分辨率（HR）航拍图像来监视灾区，因为它们可以提供丰富的信息。然而，受硬件设备和成像环境的限制，所捕获的航拍图像的分辨率可能无法满足实际应用的需求。图像超分辨率（SR）是一种以后处理方式提高捕获的图像分辨率的有效方法。最近，卷积神经网络（CNN）在图像SR中已显示出巨大的成功。但是，由于需要大量的存储和计算资源，因此这些CNN模型无法轻松地应用于实际情况。为了减少资源消耗，我们需要减少网络参数。递归网络可以有效地减少网络参数，从而激励我们探索更有效的图像SR方法。在本文中，我们提出了一种深度递归密集网络（DRDN）来重建HR航拍图像。在DRDN中，所提出的递归密集块（RDB）可以充分提取丰富的局部特征，并自适应融合LR图像的不同层次特征以进行HR图像重建。另外，在DRDN中RDB的递归方式可以有效地减少网络参数。航空影像的实验结果证明了我们提出的方法的优越性。另外，在DRDN中RDB的递归方式可以有效地减少网络参数。航空影像的实验结果证明了我们提出的方法的优越性。另外，在DRDN中RDB的递归方式可以有效地减少网络参数。航空影像的实验结果证明了我们提出的方法的优越性。