In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we propose a multi-scale enhancement network (MEN), which uses multi-scale features of remote sensing images to enhance the network’s reconstruction capability. Specifically, the network extracts the coarse features of LR remote sensing images using convolutional layers. Then, these features are fed into the multi-scale enhancement module (MEM) proposed by this network, which uses a combination of convolutional layers with multiple convolutional kernel sizes to refine the extraction of multi-scale features, and finally, the final reconstructed image is generated by the reconstruction module. Extensive experiments show that MEN achieves significant reconstruction advantages in both objective and subjective aspects.

Recently, the performances of deep convolution neural networks (CNNs)-based single-image super-resolution (SISR) have been significantly improved. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper networks and ignore the potential relationship between multi-scale features, leading to the limited representation ability of the reconstructed network. To address this problem, we propose a new multi-scale information polymerization network (MIPN). Specifically, we propose a multi-scale information polymerization block (MIPB), which uses convolution layers of different convolution kernel sizes to extract multi-scale image features, and effectively polymerizate the extracted features together to obtain fine image features. Moreover, we also propose a shallow residual block in MIPB. Compared with the traditional convolution layer, this proposed block can effectively extract image features without increasing the number of parameters. Extensive experiments show that the proposed method performs better than several state-of-the-art methods in quantitative and visual quality indicators.

In recent years, intelligent processing of satellite videos has emerged as a significant research focus within the field of remote sensing, driven by the growing demand for enhanced spatial resolution. This need has led to increased interest in satellite video super-resolution (SVSR) algorithms, which aim to improve the quality of satellite imagery. However, many existing SVSR methods tend to neglect the global dependencies among frames in satellite videos, resulting in an incomplete utilization of spatio-temporal feature information. To tackle this issue, we propose a novel non-local spatio-temporal bidirectional recurrent network specifically designed for SVSR applications. Our approach employs a gate-guided deformable alignment module that effectively enhances feature alignment and fusion using a dynamic gating mechanism. This allows the network to adaptively focus on relevant features during the reconstruction process. Furthermore, we introduce a non-local spatio-temporal fusion module that integrates both temporal and spatial relationships over long sequences of frames, ensuring a comprehensive extraction of feature information. Through extensive experiments, our proposed method demonstrates superior performance compared to state-of-the-art SVSR techniques in terms of reconstruction quality. Additionally, it demonstrates outstanding performance in downstream satellite video applications, showcasing its potential in satellite video processing tasks. The source code is publicly available at https://github.com/Yu-Wang-0801/NSBRNet.