Single-frame image super-resolution (SISR) technology in remote sensing is improving fast from a performance point of view. Deep learning methods have been widely used in SISR to improve the details of rebuilt images and speed up network training. However, these supervised techniques usually tend to overfit quickly due to the models’ complexity and the lack of training data. In this paper, an Improved Deep Recursive Residual Network (IDRRN) super-resolution model is proposed to decrease the difficulty of network training. The deep recursive structure is configured to control the model parameter number while increasing the network depth. At the same time, the short-path recursive connections are used to alleviate the gradient disappearance and enhance the feature propagation. Comprehensive experiments show that IDRRN has a better improvement in both quantitation and visual perception.

从性能的角度来看，遥感中的单帧图像超分辨率（SISR）技术正在快速改进。深度学习方法已在SISR中广泛使用，以改善重建图像的细节并加快网络培训的速度。但是，由于模型的复杂性和缺乏训练数据，这些监督技术通常倾向于快速过拟合。本文提出了一种改进的深度递归残差网络（IDRRN）超分辨率模型，以降低网络训练的难度。深度递归结构配置为在增加网络深度的同时控制模型参数数。同时，短路径递归连接用于减轻梯度消失并增强特征传播。