# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 3 Methods
### Forward Fusion Enhancement Module (FFEM)  
Due to the complexity of remote-sensing images [47], efficiently utilizing hierarchical information is crucial. We design FFEM to achieve more suitable multi-level feature fusion and enhancement. Specifically, FFEM includes a feature fusion phase and a feature enhancement phase. In the feature fusion phase, we design a forward fusion structure, as shown in Figure 4a. Once the multi-level features are fed into the FFEM, each level's features first undergo a \(1\times 1\) convolution to halve the number of channels, followed by activation through the ReLU function (ReLU operations are omitted in Figures 2 and 3), and finally are concatenated in a forward sequential manner. This can be expressed as follows:  
\[T_{i}= \begin{cases}\text{ReLU}(\text{Conv}(F_{i})),&i=1\\ \text{Conv}(\text{Concat}(T_{i-1},\text{ReLU}(\text{Conv}(F_{i})))),&i=2, \ldots,n-1\\ \text{Concat}(T_{i-1},\text{ReLU}(\text{Conv}(F_{i}))),&i=n\end{cases}  \tag{9}\]  
where \(Concat(\cdot)\) and \(Conv(\cdot)\) denote the concatenation operation along the channel dimension and the \(1\times 1\) convolution. In the task of super-resolution for structurally complex remote-sensing images, it is crucial to efficiently capture more abstract and semantically rich global information. Our forward fusion structure allocates more channels to deeper features, thereby reducing the inevitable loss of high-level feature information due to  
Figure 3: Structure of Dual Attention Fusion Enhancement Block (DAFEB). information loss during dimensionality reduction. By leveraging high-level features more effectively, we can mitigate the difficulty of global information capture caused by the local receptive field of CNNs. Utilizing the rich semantic information and contextual relationships contained in high-level features allows us to better handle the reconstruction of complex structures and global information in RSIs. In addition, our strategy also integrates low-level features, enriching a wealth of edge and texture details, thus achieving a better balance between global information and detailed information. In the feature enhancement phase, to balance both the performance and lightweight nature of the model, we design an efficient dual-branch feature enhancement structure. By feeding the fused features into the two modules, CCA and SCGC, we achieve rescaling and refinement processes. The structure of RSI is complex, and it is inaccurate to obtain the channel descriptors solely through Global Average Pooling (GAP). Since variance can reflect the richness of information in feature maps [45], we introduce CCA [15] to highlight the most informative features. This can be represented as follows:  
\[x_{\text{out}}=\mathcal{H}_{CCA}(x) \tag{10}\]  
where \(\mathcal{H}_{CCA}(\cdot)\) denotes the CCA operation. Here, we briefly describe the computation of CCA. As shown in Figure 4b, given a set of feature maps, we compute the sum of their standard deviations and means. This summation is then passed through a sequence of nonlinear functions: \(\text{Conv1}\rightarrow\text{ReLU}\rightarrow\text{Conv1}\). Subsequently, the sigmoid function is utilized to generate a set of combination coefficients ranging from 0 to 1. Finally, we multiply the input feature maps by these combination coefficients to obtain the output feature maps. For SCGC (as shown in Figure 4c), the features are first scaled down by the AdaptiveMaxPool2D function with a kernel size of 7 and a stride of 3 in order to obtain a large receptive field. Then, after tuning by group convolution and channel shuffle operations, the features are upsampled back to the input size using a bilinear interpolation function. Figure 4: Illustrations of the proposed Forward Fusion Enhancement Module (FFEM). (**a**) The forward fusion structure by using forward sequential concatenation is illustrated by taking four blocks as an example. (**b**) The Contrast-aware Channel Attention (CCA). (**c**) The Self-Calibrated Group Convolution (SCGC). (**d**) The Group Residual Shuffle Block (GRSB).