## 3 Proposed Method
### Feature Fusion Module and Multi-Scale Skip Connection Based on Residual Network
#### 3.3.1 Feature Fusion Module  
Feature fusion between different scales is usually achieved by simple operations, such as summing or splicing. However, simple summation or splicing does not adequately achieve feature fusion between different scales, because these simple operations only use fixed linear operations to achieve feature fusion, and it is not known whether such operations are suitable for a specific target. Therefore, to better fuse features with inconsistent semantics and scales, this paper proposes a feature fusion module to achieve the fusion of features of different scales and to solve the problem of inconsistent semantics and scales when fusing features of different scales. Firstly, the channel attention module is introduced in detail. The usual channel attention module uses global average pooling to map the feature map of each channel to a single value. However, this operation is not applicable for recovery tasks with high image detail requirements, and it tends to ignore the detailed features of images. Therefore, this paper combines image features with channel attention, such that the channel attention module focuses on global features, as well as local detail features of the image. As shown in Figure 3, the local context is combined with the global context inside the channel attention module. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Input\(\rightarrow\) Output Shape** & **Layer Information** \\ \hline \multirow{5}{*}{Encoded Module} & \((h\), \(w\), \(3)\) \(\rightarrow\)\((h\), \(w\), \(64)\) & ReflectionPad(3), Conv-(N64, K7, S1, P0), IN, ReLU \\  & \((h\), \(w\), \(64)\) \(\rightarrow\)\((\frac{1}{6}\), \(\frac{1}{6}\), \(\frac{1}{6}\)) & Conv-(N128, K3, S2, P1), IN, ReLU \\  & \(\left(\frac{1}{2},\ \frac{1}{6},\ 128\right)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6}\), \(\frac{1}{6}\)) & Conv-(N256, K3, S2, P1), IN, ReLU \\  & \(\left(\frac{1}{4},\ \frac{1}{6},\ 256\right)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6}\)) & Conv-(N512, K3, S2, P1), IN, ReLU \\ \hline \multirow{5}{*}{Residual Module} & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 512)\) & ReflectionPad(1), Conv-(N512, K3, S1, P0), IN, ReLU \\ \hline \multirow{5}{*}{Decoded Module} & \((\frac{1}{6},\ \frac{1}{6},\ 512)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 256)\) & Deconv-(N256, K3, S2, P1), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 256)\) \(\rightarrow\)\((\frac{1}{6},\ \frac{1}{6},\ 128)\) & Deconv-(N128, K3, S2, P1), IN, ReLU \\  & \((\frac{1}{6},\ \frac{1}{6},\ 128)\) \(\rightarrow\)\((h,\,w,\ 64)\) & Deconv-(N64, K3, S2, P1), IN, ReLU \\  & \((h,\ w,\ 64)\) \(\rightarrow\)\((h,\ w,\ 3)\) & ReflectionPad(3), Conv-(N3, K7, S1, P0), Tanh \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Generator network architecture. module.