# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 4 Experimental Results
### Ablation Study  
While constructing the network, we noted that the image output at each node could also be used as a basis for optimizing the effectiveness of the network, i.e., to achieve a better visual quality for these images. Therefore, we utilized the image evaluation metrics to measure the strength of our final network.  
Figure 11 demonstrates the effect of extracting gradient information in our feature enhancement stage. In order to better serve as input features for the next stage, we focused on the integrity and continuity of the edge information, so we set different depth networks for learning, where (d) only used the higher-level information network architecture with richer gradient information. As can be seen, compared to (b) and (c), the edge image (d) was sufficiently clear and exhibited a better visual output when only the underlying information was used.  
Figure 12 shows the ablation comparison experiments in the final acquired reflection layer, where the single-stage network in (c) and (d) refers to the network that did not indirectly predict the transmission layer information but directly predicted the reflection layer, and (h) represents the final completed experimental configuration. Through comparison, it can be seen that the complete loss condition was more capable of removing the information in the transmission layer, thus preserving and highlighting the content of the reflection layer. For a more quantitative analysis, Table 2 lists the corresponding quality indicator values. It can be seen that (h) possessed a clearer visual quality than the other experimental results, which demonstrates that the proposed method could obtain more information about the reflection layer after removing the transmission layer.  
Figure 10: LPIPS value line chart of 50 test image sets. The comparison methods involved include [PERSON] et al. (2018), [PERSON] et al. (2019), [PERSON] et al. (2020), [PERSON] et al. (2019), and ours.  
Figure 13 shows the predicted images for the high-resolution images, where the high-resolution images are the input images that were not transmitted by the OSN, and the predicted images were the result of retraining the network architecture, which can be seen to have removed the transmission layer to a greater extent in terms of visual recovery. For  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **LPIPS \(\downarrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** \\ \hline Figure 12c & 0.578 & 0.710 & 20.080 \\ Figure 12d & 0.679 & 0.674 & 17.423 \\ Figure 12e & 0.677 & 0.696 & 18.198 \\ Figure 12f & 0.747 & 0.699 & 18.616 \\ Figure 12g & 0.676 & 0.693 & 18.010 \\ Figure 12h & 0.626 & 0.743 & 22.444 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative analysis of quality indicators for the images in Figure 12.  
Figure 11: Ablation experiment for gradient profile prediction; (**a**) represents the input image and (**b–d**) represent the edge gradient ablation results by setting different network depths; to be specific, the size of (**b**) was \(16\text{C}\times H/16\times W/16\), (**c**) was \(8\text{C}\times H/8\times W/8\), and (**d**) was \(4\text{C}\times H/4\times W/4\).  
Figure 12: Ablation experiment for reflection layer extraction; (**a**) represents the input image, (**b**) represents the ground-truth background image of the reflection layer, (**c**,**d**) represent a single-stage network without \(L_{\text{texture}}\) and \(L_{\text{TV}}\), respectively; (**e–g**) represent a two-stage network without \(L_{\text{excl}}\), \(L_{\text{texture}}\), and \(L_{\text{TV}}\), respectively; and (**h**) represents the final completed experimental configuration.  
the purpose of this study, this means that there is still room for improvement in terms of image super-resolution and recovery of auxiliary edge information.