# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 3 Proposed Method
### Network Architecture  
The framework of the proposed global-local blur disentangling network is shown in Figure 2. The network is composed of 4 modules, a global blur feature extraction module, a local blur feature extraction module, a branch attention module, and a reconstruction module. The network takes the blurry image as input. First, the two-branch network extracts the global and local blur features of the input, respectively. Then, the branch attention module dynamically fuses the dual branch features to obtain the attention mask and element-wise multiply the local branch features, so that the local branch module focuses on the local features. Finally, under the combined effect of the updated weighted local blur feature and the global blur feature, the reconstruction network is used to restore a clear image. The four modules are introduced in detail as follows. **Local blur feature extraction branch module. ** The top branch of the framework is designed to extract the local blur features of the input image \(x\). The architecture of this blur branch refers to Reference [37]. The module adopts an encoder-decoder architecture with multiple long and short skip connections. The encoder contains 3 scales, and the input and output of each scale are connected across layers through long-skipping connections. Each scale is composed of 6 residual modules, and each residual module is composed of two convolution layers with a kernel of \(3\times 3\) and a stride of 1. To enhance the fusion between low-level feature features and high-level features, short-skipping connections and long-skipping connections are adopted to fuse the feature maps of different layers. The decoder consists of two transposed convolutions and three convolutional layers. Each transposed convolution enlarges the spatial scale of the feature map with 2 times. Finally, we use 3 convolutional layers to reconstruct the restored image. We use the output \(\Psi_{L}\) of the first convolution of the decoder as the output of the local blur feature branch, and then as the input of the branch attention module. **Global blur feature extraction branch module. ** The architecture of the bottom branch is designed same as the top branch. To constrain the module to extract the global blur features of the input, we use global uniform blur data to train the branch and fix the weight of the branch after the network is converge to ensure that the branch pays more attention to global blur features. The specific training dataset and training strategy will be introduced in detail in Section 4. **Branch attention module. ** We do not simply adopt concatenation or multiplication on the output feature maps of the two branches. Instead, we designed a branch attention module to dynamically fuse the global and local blur features and guide the processing of restoration. As shown in Figure 2, the branch attention module is composed of two operations, namely element-wise multiplication and addition. Local blur is non-uniform in different spatial positions. Therefore, we use the concatenation of the three features as the input of the attention module, so that the attention module can obtain attention map via different feature maps, which includes the blurry image \(x\), the output of the local blur branch \(\Psi_{L}\), and the output of the global blur branch \(\Psi_{G}\). There are two convolutional layers \(M(\cdot)\) is adopted to extract the local weight mask feature map. It multiplies the corresponding elements with the output \(\Psi_{L}\) of the local blur branch to obtain the local blur features. The purpose of this step is to improve the ability to extract local blur features. Then, add the weighted local blur feature and the output \(\Psi_{G}\) of the global blur feature branch to obtain the output \(\Psi_{B}\) of the branch attention module. The process is shown in (1):  
\[\Psi_{B}=\Psi_{L}\odot M(x,\Psi_{L},\Psi_{G})+\Psi_{G}, \tag{1}\]  
Figure 2: Dual-branch decoupling network architecture. where \(\odot\) represents element-wise multiplication.