# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Transmission Removal for Photographer Identification  
For the application of photographer identification, Figure 14 shows the transmission removal effects of our method. The recovered facial information can help us identify personal information. In detail, the Internet visual search tool (e.g., Bing Visual Search by Microsoft ([[https://www.bing.com/visualsearch](https://www.bing.com/visualsearch)]([https://www.bing.com/visualsearch](https://www.bing.com/visualsearch)), accessed on 29 July 2022)) and DeepFace [44] are used to evaluate the efficacy of photographer identification of the mixture images and estimated images. We selected the related Hollywood celebrity portraits from CACD2000 [45], as shown in Figure 15 as the target images for verification with our test images on DeepFace. Table 6 lists the verification results of two leading recognition platforms, where for the visual search tool, the output is the name of the image subject information, and for DeepFace, the output includes the verification results and matching distance with the target images. As can be seen from the table, most of the images recovered by other methods cannot be matched with the target image or even detect the face. In comparison, our results in the visual search tool can recognize faces well and identify the corresponding identity information. Moreover, the face matching by DeepFace is achieved with numerical output,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LPIPS (\(\downarrow\))** & **SSIM (\(\uparrow\))** & **PSNR (dB) (\(\uparrow\))** \\ \hline W/O SR module and content loss & 0.647 & 0.588 & 18.611 \\ W/O SR module & 0.568 & 0.844 & 23.313 \\ W/O content loss & 0.556 & 0.813 & 21.219 \\ Complete & 0.508 & 0.800 & 24.883 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative ablation study of Ours(a) network for the SR module and content loss.  
Figure 12: Comparisons of visual effects of the proposed network with or without SR module for Ours(b). The green boxes are partially enlarged for a better view.  
Figure 13: Comparisons of visual effects of the proposed network with or without content loss for Ours(b). The green boxes are partially enlarged for a better view.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LPIPS (\(\downarrow\))** & **SSIM (\(\uparrow\))** & **PSNR (dB) (\(\uparrow\))** \\ \hline W/O SR module and content loss & 0.521 & 0.813 & 23.769 \\ W/O SR module & 0.413 & 0.844 & 28.780 \\ W/O content loss & 0.437 & 0.811 & 25.559 \\ Complete & 0.396 & 0.851 & 29.059 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative ablation study of Ours(b) network for the SR module and content loss.  
which means that we can still recognize face information even if there is an incomplete match.  
Figure 16 shows the effects of different style image selection in the photographer identification application. The results in the third column are obtained by using a fixed face image as the style image. In contrast, the fourth column is obtained by using the corresponding image with semantic information as the style image. It can be seen that the results with semantic style images perform better than those with a fixed style image in terms of skin tone, eyebrows, and hair color. When the reflection in the mixture image is dim, the color images with the corresponding semantic information can achieve image colorization, as in the top row of Figure 16.  
Figure 16: Comparisons of visual effects of the proposed network with a fixed style image or with a semantic style image. The top and bottom rows correspond to dim and vivid reflection images with portraits.  
Figure 14: Four recovery examples of the reflection layers containing portrait scenes. (**a**) Mixture image, (**b**) [13], (**c**) [14], (**d**) [16], (**e**) [17], (**f**) Ours(a), (**g**) Ours(b).  
Figure 15: Selected target images for verification from CACD2000 dataset.