# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 1 Introduction  
Remote-sensing images (RSIs) significantly contribute to earth and environmental sciences by providing diverse observational data. They offer critical support for various tasks, such as agricultural and forestry monitoring [1, 2], disaster early warning [3, 4], military reconnaissance [5], and industrial manufacturing [6]. Therefore, providing high-resolution RSIs is essential for a more accurate and objective representation of the targets and contexts. However, remote-sensing data are affected by adverse factors such as long distances and wide viewing angles during the acquisition process. For edge devices with limited computational resources, coupled with the limitations of their hardware factors and unfavorable imaging conditions, it is even more difficult to acquire high-quality RSIs. RSI Super-Resolution (RSISR) focuses on constructing a nonlinear mapping relationship between a pair of high-resolution and low-resolution remote-sensing images. Compared with upgrading remote-sensing imaging equipment, researching efficient RSISR algorithms at the software level can not only effectively improve the resolution of images but also save significant costs. Therefore, using RSISR technologies to improve the resolution of remote-sensing images has become a key research direction for scholars. Since the pioneering SR method SRCNN [7] was proposed, the field of SR has grown tremendously [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. The success of natural image SR techniques has spurred the development of RSISR [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. RSISR builds upon natural image SR research by incorporating specific improvements tailored to remote-sensing images, such as designing loss functions suited to the unique features of RSI [33], employing a two-stage design that utilizes spatial and spectral knowledge from adjacent bands [24], and creating degradation models that reflect remote-sensing scenarios [34]. As research progresses, although the performance of these RSISR models has continually improved, they face challenges in effectively managing the inherent complexity of the networks. We take examples from well-known SR methods such as RCAN [14], SAN [35], and SwinIR [36]. When applied to RSISR tasks, RCAN [14] has about 15M parameters, SAN [35] has about 26M parameters, and large variants of SwinIR [36] have upwards of 20M parameters. Recently, RSISR methods based on the diffusion model [37; 38; 39] and Mamba [40] have become new research hotspots, but their model parameter number is similarly large. The large number of parameters greatly limits their deployment on resource-constrained devices. Therefore, our work aims to design a lightweight and efficient RSISR network to recover remote-sensing images. In the field of RSISR, several successful lightweight models have emerged. The attention-based multi-level feature fusion (AMFF) in AMFFN [41] differs from common feature fusion methods [15; 16; 42; 43; 44]. It adopts a grouped approach to progressively fuse features from different blocks, which fully leverages multi-level features. However, the AMFF adopts a 1 \(\times\) 1 convolution fusion method, which significantly increases the number of parameters when the number of input channels is large. Additionally, it only uses the Contrast-aware Channel Attention (CCA) [15] module in the enhancement stage, which provides relatively weak performance. RFCNet [45] addresses feature enhancement by designing the Residual Feature Calibration Block (RFCB), which further refines and rescales input features through a clever dual-branch structure, significantly improving the model's fitting capability. However, RFCB employs channel separation, causing each branch to enhance only half of the feature information, which limits the feature representation capability. Moreover, the use of multiple 3 \(\times\) 3 convolution layers leads to a significant increase in model parameters.