# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
## 3 Improved Imaging Logging Image Restoration Model for GAN
### Image Generation Networks
#### 3.1.2 Multi-Level Feature Extraction Module  
To further extract feature information from the complete region of the image, a multi-scale feature extraction module is introduced into the generative network structure for extracting feature information at different scales. In addition, a channel attention mechanism is introduced into the network with the aim of suppressing feature information that has less impact on the area to be restored in the imaging logging image, while assigning higher weights to important features. In order to focus more on the network features transmitted by the front layer network, a residual block is introduced into the network to reduce the content blurring and artefacts that may occur at the boundary to be repaired due to upsampling. The channel attention mechanism and the residual block are used in conjunction to form the channel attention residual block (CAR). Before the feature information is extracted using the mid- and low-frequency branches, a feature extraction module (FEM) consisting of 7 \(\times\) 7, 5 \(\times\) 5, and 4 \(\times\) 4 convolution, BN, and LReLU is used to extract \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Convolution Module** & **Input** & **Output** \\ \hline
1 & Conv3\(\times\)3 + BN + ReLU & 3 \(\times\) 256 \(\times\) 256 & 32 \(\times\) 256 \(\times\) 256 \\
2 & Conv4\(\times\)4 + BN + LReLU + Inception & 32 \(\times\) 256 \(\times\) 256 & 64 \(\times\) 128 \(\times\) 128 \\
3 & Conv4\(\times\)4 + BN + LReLU + Inception & 64 \(\times\) 128 \(\times\) 128 & 128 \(\times\) 64 \(\times\) 64 \\
4 & Concat + Conv4\(\times\)4 + BN + LReLU + Inception & 128 \(\times\) 64 \(\times\) 64 & 256 \(\times\) 32 \(\times\) 32 \\
5 & Conv4\(\times\)4 + BN + LReLU + Inception & 256 \(\times\) 32 \(\times\) 32 & 256 \(\times\) 16 \(\times\) 16 \\
6 & Concat + Conv4\(\times\)4 + BN + LReLU + Inception & 256 \(\times\) 16 \(\times\) 16 & 512 \(\times\) 8 \(\times\) 8 \\
7 & DSCR & 512 \(\times\) 8 \(\times\) 8 & 512 \(\times\) 4 \(\times\) 4 \\
8 & DSCR & 512 \(\times\) 4 \(\times\) 4 & 512 \(\times\) 2 \(\times\) 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the structural components of the downsampling network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Deconvolution Module** & **Input** & **Output** \\ \hline
1 & DSCR & 1024 \(\times\) 4 \(\times\) 4 & 512 \(\times\) 4 \(\times\) 4 \\
2 & BI + Deconv3\(\times\)3 + BN + ReLU & 1024 \(\times\) 8 \(\times\) 8 & 512 \(\times\) 8 \(\times\) 8 \\
3 & BI + Concat + Deconv3\(\times\)3 + BN + ReLU & 1024 \(\times\) 16 \(\times\) 16 & 256 \(\times\) 16 \(\times\) 16 \\
4 & BI + Deconv3\(\times\)3 + BN + ReLU & 512 \(\times\) 32 \(\times\) 32 & 256 \(\times\) 32 \(\times\) 32 \\
5 & BI + Concat + Deconv3\(\times\)3 + BN + ReLU & 512 \(\times\) 64 \(\times\) 64 & 128 \(\times\) 64 \(\times\) 64 \\
6 & BI + Deconv3\(\times\)3 + BN + ReLU & 256 \(\times\) 128 \(\times\) 128 & 64 \(\times\) 128 \(\times\) 128 \\
7 & BI + Deconv3\(\times\)3 + BN + Sigmoid & 96 \(\times\) 256 \(\times\) 256 & 3 \(\times\) 256 \(\times\) 256 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of upsampling network structure components. features from the input image and output a feature map of size 256 \(\times\) 16 \(\times\) 16. The overall structure of the FEM module and the CAR module is shown in Figure 6.  
The medium-spatial-resolution branch consists of an 8-layer network, which, after convolution and batch normalisation, outputs a feature map with a size of 1.5 mm. The low-spatial-resolution branch consists of a 7-layer network, which, after convolution, outputs a feature map with a size of 1.5 mm. Bilinear interpolation (BI) is used between the convolution modules in the medium- and low-spatial-resolution branches to increase the image size, recover the image size, and increase the resolution of the image. Finally, the feature maps output from the two branches are connected to the corresponding network layers in the generation module to achieve multi-scale feature extraction.