# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
## 5 Example Application and Analysis of Results
### Analysis of Experimental Results  
Based on the experimental comparison results obtained from the real imaging logging data in Section 5.1, the authors make the following theoretical analysis. As can be seen from Figure 9c, the bilinear interpolation, normalisation, ReLU, and LeakyReLU activation functions are used in this model to improve the quality of the images generated by the generative network, and the loss functions of the training and validation sets start to level off at about 20 iterations. Figure 9a,b shows the mean structural similarity measure and the peak signal-to-noise ratio with training, respectively, and based on the trend of the curves, it can be seen that there is no overfitting in this improved GAN imaging logging image restoration model. In Figure 10, each row of images from left to right shows the original undeficient image, the image with mask, and the image after restoration using different models. It can be observed that the discriminative network of the GLCIC model is well designed to discriminate the quality of the images generated by the generative network globally and locally, but the restored images are partially blurred because the generative network uses inflated convolution but does not add an appropriate attention mechanism. PDGAN uses spatial probability diversity normalisation and loss of perceptual diversity, and considers that repairing the region to be repaired requires giving higher weights to the surrounding pixels of the missing region, and therefore does not pay enough attention to the pixels as a whole, so that artefacts may be produced when the edges of the image are missing from repair. In contrast, the model in this paper designs a deep separable convolutional residual block, an Inception module, and a multi-scale feature extraction module in the generative network in order to generate higher quality images with clear content and a coherent semantic structure, and the discriminative network designs global and local discriminations, focusing more on the discriminations of edge parts from both global and local aspects to further enhance the image realism. By inviting observers for subjective observation, there is a better restoration effect compared to the other three restoration models. Table 6 shows the quantitative comparison results of the different models on imaging log images with different missing ratios, and it can be seen that the improved model performs well in terms of PSNR and MSSIM. The data in Table 7 show that the PSNR and MSSIM of the model (with a missing ratio of (0.1-0.2)) are improved to different degrees when the designed modules are gradually introduced, e.g., the MSSIM of the model rises by 0.082 when using the depth-separable convolutional residual block in the generative network; the MSSIM of the model rises by 0.109; the introduction of the multi-scale feature extraction module increased the MSIM by 0.144; the introduction of the channel attention residual block increased the MSIM by 0.150; and the final experiment \begin{table}
\begin{tabular}{c c c c c c} \hline Method & (0.01–0.1) & (0.1–0.2) & (0.2–0.3) & (0.3–0.4) & (0.4–0.5) \\ \hline model(original) & 27.347/0.786 & 25.472/0.703 & 22.253/0.657 & 20.352/0.589 & 20.462/0.563 \\ model(DSCR) & 29.527/0.805 & 27.963/0.785 & 23.869/0.687 & 21.735/0.602 & 20.984/0.583 \\ model(Inception) & 30.315/0.824 & 29.153/0.812 & 25.752/0.705 & 23.562/0.658 & 22.074/0.614 \\ model(FEM) & 32.573/0.869 & 30.751/0.847 & 27.147/0.764 & 25.769/0.705 & 24.351/0.673 \\ Model(CAR) & 35.884/0.927 & 32.574/0.853 & 29.749/0.806 & 27.734/0.749 & 26.048/0.702 \\ model(Full) & 37.126/0.958 & 35.276/0.916 & 33.153/0.865 & 30.528/0.849 & 27.938/0.785 \\ \hline \end{tabular}
\end{table} 
Table 7: Differences in performance of improved GAN under ablation experiments (PSNR(dB)/MSSIM). using the complete model of the imaging logging image restoration method with improved GAN increased the MSSIM by 0.213 compared to the initial one.