# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
## 3 Improved Imaging Logging Image Restoration Model for GAN
### Image Discriminant Network  
As the underlying network architecture of the generative network is FCN, the restoration effect is still insufficient to support the evaluation and interpretation of imaging logging images, despite the design of a multi-scale feature extraction module by combining the channel attention mechanism and residual connection. Through an in-depth analysis of GAN and [PERSON] et al. [20], it is found that the discriminative network architecture in the model proposed by [PERSON] et al. can significantly improve the model restoration effect, so in this paper a discriminative network is designed and built with reference to its discriminative module as well. The discriminative network uses the adversarial idea to play the restored image generated by the generative network off the real image, and gradually \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Convolution Module** & **Input** & **Output** & **Branch** \\ \hline
1 & Conv5\(\times\)5 + BN + LReLU & 256 \(\times\) 16 \(\times\) 16 & 256 \(\times\) 8 \(\times\) 8 & \\
2 & Conv3\(\times\)3 + BN + ReLU + CAR & 256 \(\times\) 8 \(\times\) 8 & 256 \(\times\) 8 \(\times\) 8 & \\
3 & BI & 128 \(\times\) 8 \(\times\) 8 & 128 \(\times\) 16 \(\times\) 16 & \\
4 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 16 \(\times\) 16 & 128 \(\times\) 16 \(\times\) 16 & \\
5 & BI & 128 \(\times\) 16 \(\times\) 16 & 128 \(\times\) 32 \(\times\) 32 & Middle \\
6 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 32 \(\times\) 32 & 128 \(\times\) 32 \(\times\) 32 & \\
7 & BI & 128 \(\times\) 32 \(\times\) 32 & 128 \(\times\) 64 \(\times\) 64 & \\
8 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 64 \(\times\) 64 & 128 \(\times\) 64 \(\times\) 64 & \\ \hline
1 & Conv5\(\times\)5 + BN + LReLU & 256 \(\times\) 16 \(\times\) 16 & 256 \(\times\) 8 \(\times\) 8 & \\
2 & Conv5\(\times\)5 + BN + LReLU & 256 \(\times\) 8 \(\times\) 8 & 128 \(\times\) 4 \(\times\) 4 & \\
3 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 4 \(\times\) 4 & 128 \(\times\) 4 \(\times\) 4 & \\
4 & BI & 128 \(\times\) 4 \(\times\) 4 & 128 \(\times\) 8 \(\times\) 8 & Low \\
5 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 8 \(\times\) 8 & 128 \(\times\) 8 \(\times\) 8 & \\
6 & BI & 128 \(\times\) 8 \(\times\) 8 & 128 \(\times\) 16 \(\times\) 16 & \\
7 & Conv3\(\times\)3 + BN + ReLU + CAR & 128 \(\times\) 16 \(\times\) 16 & 128 \(\times\) 16 \(\times\) 16 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the structural components of multi-scale feature extraction networks. Figure 6: FEM and CAR structure diagram. updates the parameters of the generative and discriminative networks according to the results, so that the image generated by the generative network can deceive the discriminative network and has clear and reasonable texture details in the visual effect. In order to improve the restoration quality and to enable the model to better focus on the missing areas of the image, the discriminant network structure studied by [PERSON] et al. [21] was used to improve on this structure, and a local discriminant network was used to assist the global discriminant network in discriminating local areas of the image. The overall discriminant network structure is shown in Figure 7.  
Both the global discriminative network and the local discriminative network are built based on a full convolutional neural network that compresses the image into small feature vectors. The networks are basically similar except for the number of layers, and an overview of their respective compositions is shown in Tables 4 and 5. The global discriminative network takes as input the whole image rescaled to a size of 256 \(\times\) 256 pixels, and its network structure consists of five convolutional layers, all of which use 5 \(\times\) 5 convolutional kernels, BN, and LReLU, and reduce the feature map in 2 \(\times\) 2 pixel steps simultaneously.