# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 1 Introduction
FeNet [46] constructs Lightweight Lattice Blocks (LLBs) based on channel separation and effectively integrates multi-level feature information through nested module design. In multi-level feature fusion, the Backward Fuse Module (BFM) [16] employed by FeNet can extract more contextual information at different levels more effectively than 1 \(\times\) 1 convolution. However, LLB extracts features from only half of the channels, which results in poorer feature extraction. Additionally, its nested module structure is overly complex, resulting in significant redundancy. Moreover, the backward sequential concatenation used by BFM leads to higher-level features being compressed more than lower-level features. However, high-level features typically contain more semantic information and contextual relationships, which are crucial for reconstructing complex structures and global information. Given the complexity of RSIs [47], the excessive loss of high-level feature information is unreasonable. To address the limitations of the existing methods, we propose the Dual Attention Fusion Enhancement Network (DAFEN). Meanwhile, to accommodate devices with extremely limited hardware capabilities, we further designed an extremely lightweight version, DAFEN-S, by reducing the number of channels, with the model parameters reduced to only 188 K. As shown in Figure 1, our DAFEN sufficiently demonstrates the competitive advantages of the model compared with other lightweight models and achieves a better balance between the complexity of the network and the reconstruction accuracy. Specifically, feature refinement and fusion section of DAFEN is composed of four stacked Dual Attention Fusion Enhancement Blocks (DAFEBs) and a Forward Fusion EnhancementModule (FFEM). The features extracted by the shallow-layer DAFEB are not only fed into the next-layer DAFEB for further extraction and refinement but also input into the FFEM for multi-level feature fusion and enhancement. Furthermore, each DAFEB consists of three Channel-Spatial Lattice Blocks (CSLBs) and a Forward Fusion Enhancement Module (FFEM), with a structure similar to the feature refinement and fusion section of DAFEN. Notably, we introduce a Context Enhancement Module (CEM) [48] at the end of the DAFEB to further enhance the feature information. By employing the extremely lightweight CEM, the receptive field is further enlarged to extract more contextual information for RSISR. In addition, the core module of the DAFEN consists of the Forward Fusion Enhancement Module (FFEM) and Channel-Spatial Lattice Block (CSLB). Firstly, the FFEM consists of a multi-level feature fusion stage and a feature enhancement stage. On the one hand, we adopt a forward sequential concatenation approach, which reduces the loss of high-level feature information while supplementing low-level feature details, thereby fully utilizing multi-level features to extract richer contextual information. On the other hand, we design a dual-branch structure for feature enhancement. In the upper branch, we design a Self-Calibrated Group Convolution (SCGC), which self-calibrates based on the expression of local features in a deep receptive field, further refining the features. In the lower branch, we introduce CCA [15], which calculates weight coefficients through CCA to obtain a better channel attention vector. Secondly, the CSLB adopts a dual-branch design based on channel separation, making feature extraction and information interaction extremely lightweight. Unlike other lattice blocks [16; 46; 49], we design the Group Residual Shuffle Block (GRSB) and the Channel-Spatial Attention Interaction Module (CSAIM). The GRSB extracts features through group convolutions and enhances information flow via channel shuffle, which enables GRSB to achieve feature representation similar to 3 \(\times\) 3 convolution while using fewer parameters.