## 1 Introduction  
Satellite remote sensing imaging is a popular and efficient method to achieve indirect acquisition of surface information, by analyzing the radiation characteristics and electromagnetic wave reflection characteristics of ground objects. An ideal image is set to contain objects that are objectively and accurately identifiable, such as mountains, rivers, vehicles, and buildings. However, due to the complexity of the satellite imaging system circuit and unknown electromagnetic environment, the data acquisition process is often interfered by a bad natural environment, noise, and other factors, resulting in a lower quality of the output image. This deterioration of the quality can decrease the identification accuracy of the object and the usability of the image, thus introducing more difficulties to the follow-up research. Therefore, in current applications, additional preprocessing techniques such as noise reduction and super-resolution are usually required to improve image quality. This preprocessing is a critical stage as the data it generates serve as an information foundation can be relied on for any later earth observation, analysis, identification, and accurate judgment. For this reason, image super-resolution reconstruction technology has important research value and broad application prospects in the field of satellite remote sensing [1]. Deep learning is a branch of machine learning, which has a remarkable effect on speech and image recognition; it enables the machine to imitate human audiovisual perception and thinking, thereby solving many complex pattern recognition problems [2; 3]. In particular, the image super-resolution reconstruction technology based on deep learning has made remarkable achievements in recent years. A super-resolution convolutional neural network (SRCNN) was proposed by [PERSON] et al. [4] in 2014. This method extracts image features through convolution operation, and then optimizes model mapping parameters by continuously comparing low-resolution images with high-resolution images. As a result, a precedent was created for deep learning in the image super-resolution field. Later, an accelerated super-resolution model of a neural network (fast SRCNN, FSRCNN) was proposed by [PERSON] et al. [5], which innovatively improved sample images by utilizing a deconvolution operation in the last layer in the model, to quickly construct the super-resolution of images with different scales. In 2016, [PERSON] et al. [6] proposed a very deep super-resolution convolutional network (VDSR), which introduced residual learning to ensure that a combination of 20 convolution layers could be utilized to extract image features over a long distance, and the reconstruction results were significantly impressive. [PERSON] et al. [7] proposed an enhanced deep convolutional network (EDSR) to learn the differences in detail between high-resolution images and low-resolution images. The trained model was significant in size and optimized with fewer modules in place. Similarly, to improve the overall performance of the network, [PERSON] et al. [8] designed a deep Laplacian pyramid network for fast and accurate super-resolution (LapSRN). The Laplacian pyramid structure was adopted to obtain the reconstructed super-resolution image by layer up-sampling and fusion of residual image features. [PERSON] et al. [9] also proposed a super-resolution algorithm based on a generative adversarial network (SRGAN). The image reconstructed by SRGAN and the corresponding high-resolution image were input into a discrimination network to determine a true or false result. Through this confrontation training, the super-resolution reconstruction capability of the model was iteratively improved. However, the existing super-resolution network algorithms have been built with relatively insufficient research on satellite remote sensing imaging, where room for further improvement still exists, particularly in terms of developing a strengthened recovery effect and a lowered complexity.