# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 3 Methods
### Forward Fusion Enhancement Module (FFEM)
Finally, the final spatial statistics are obtained using the sigmoid function after residual concatenation with the input features. This process can be represented as follows:  
\[\omega=\delta\big{(}\mathcal{H}_{\uparrow}\big{(}\mathcal{H}_{\text{shuffle}} \big{(}\mathcal{H}_{\text{GGConv}}\big{(}\mathcal{H}_{\downarrow}(x_{1})\big{)} \big{)}\big{)}+x_{1}\big{)} \tag{11}\]  
where \(\omega\) denotes the attention matrix for scaling and refining the input features \(x_{1}\). \(\delta\) denotes the sigmoid activation function, \(\mathcal{H}_{\uparrow}\) represents the up-sampling operation via bilinear interpolation, \(\mathcal{H}_{\text{GGConv}}\) and \(\mathcal{H}_{\text{shuffle}}\) denote the group convolution operation and the channel shuffle operation, and \(\mathcal{H}_{\downarrow}\) denotes the down-sampling operation using the AdaptiveMaxPool2D function. After obtaining the attention matrix \(\omega\), we multiply \(\omega\) by the features refined through two GRSBs for self-calibration. Finally, we add group convolution and channel shuffle operations to further deepen the features, thereby enhancing performance. This process can be represented as follows:  
\[x_{\text{out}}=\mathcal{H}_{\text{shuffle}}(\mathcal{H}_{\text{GGConv}}( \omega\times\mathcal{H}_{\text{GRSB}}(\mathcal{H}_{\text{GRSB}}(x_{1})))) \tag{12}\]  
where \(x_{\text{out}}\) denotes the output feature and \(\mathcal{H}_{\text{GRSB}}(\cdot)\) denotes the GRSB operation. Here, we provide a detailed description of the GRSB used in SCGC and CSLB, as shown in Figure 4d. We observe that 3 \(\times\) 3 convolutions account for a significant proportion of network parameters in most lightweight super-resolution networks. This observation prompted us to consider reducing the weight of the super-resolution network by replacing 3 \(\times\) 3 convolutions with lightweight convolutions while maintaining performance. The group convolution and channel shuffle strategy of ShuffleNet [59] effectively reduces model complexity while preserving high feature extraction capability, making it suitable for deployment on resource-constrained devices. Through our experimental comparison of numerous excellent convolution modules [60; 61; 62], we verify that the combination of group convolution and channel shuffle strategy offers optimal performance for this task. By drawing inspiration from ShuffleNet, we create GRSB as a lightweight alternative to conventional convolution. Group convolution divides the original 3 \(\times\) 3 convolution operation into several groups, each containing a portion of the input and output channels, and performs convolution operations independently on each group. The results of all groups are then merged to obtain the final output feature map. Following the residual connection, we select LeakyReLU as the activation function and employ a channel shuffle operation at the end. Benefiting from the channel shuffle operation, GRSB mitigates the inter-group information isolation caused by grouping and facilitates information flow between different groups. This can be described as follows:  
\[x_{d}=\mathcal{H}_{\text{shuffle}}(\mathcal{H}_{\text{LReLU}}(x+\mathcal{H}_{ \text{GGConv}}(x))) \tag{13}\]  
where \(x\) denotes input features, \(x_{d}\) denotes output features, and \(\mathcal{H}_{\text{LReLU}}(\cdot)\) denotes the LeakyReLU operation. After the feature enhancement through the upper and lower branches, the two sets of output features undergo a 1 \(\times\) 1 convolution for fusion. Our design FFEM includes two operational stages. In the first stage, we integrate different levels of CSLB features within the DAFEB, allowing for continuous refinement and expansion of these features. In the second stage, we merge different levels of DAFEB features. Through FFEM, the interaction between feature refinement and fusion is iteratively conducted, enabling the extraction of more significant contextual information.