# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 5 Conclusions  
This paper proposes a reflection scene separation method by transmission removal to recover the highly reproduced reflection layer image from the mixed image with glass. The main framework of the proposed transmission removal network is based on GAN while taking into account the global information, where constraints of different dimensions are placed on the patch-level features of the generated images. Starting from the idea of separability of content and style in the generated images, we use different levels of contextual information to retain more details in the generated images and achieve clarity of details through interpolation and re-convolution operations of the SR module. Furthermore, we also extend our network to the application of photographer identification, which can effectively assist in exploring the secrets behind the photo. Our experiments show that both the transmission removal of the general scene and the scene with the portrait have satisfactory results. Our method no longer outputs the reflection layer as a byproduct compared to previous reflection removal methods. In addition, since we complement the perception of the reflection image on the glass image, some weak textural information in the mixture image can be perceived. Compared with the baseline method for reflection separation, whose contribution is to design a generic enhancement network to enhance the recovered reflection image, our proposed method focuses more on reducing the gap between the recovered reflection layer image and the ground truth reflection image. In fact, after our proposed network, it can also be migrated to the enhancement network in previous work for further data enhancement. However, we hold the following two perspectives: (1) the effect of recovering the reflection layer closer to the ground truth can provide more significant help for the subsequent processing; and (2) the subsequent enhancement of \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Image**} & \multirow{2}{*}{**Recognition by**} & \multicolumn{2}{c}{**Verification by DeepFace**} \\ \cline{3-4}  & & **Bing Visual Search** & **Verified** & **Distance** \\ \hline Figure 14(1-a) & An architecture & No face & N/A \\ Figure 14(1-b) & [PERSON] & No face & N/A \\ Figure 14(1-c) & No result & No face & N/A \\ Figure 14(1-d) & No result & No face & N/A \\ Figure 14(1-e) & [PERSON] & No face & N/A \\ Figure 14(1-f) & **[PERSON]** & **False** & **0.6255** \\ Figure 14(1-g) & **[PERSON]** & **True** & **0.2928** \\ \hline Figure 14(2-a) & An architecture & No face & N/A \\ Figure 14(2-b) & No result & No face & N/A \\ Figure 14(2-c) & No result & No face & N/A \\ Figure 14(2-d) & No result & No face & N/A \\ Figure 14(2-e) & No result & No face & N/A \\ Figure 14(2-f) & **[PERSON]** & **True** & **0.2827** \\ Figure 14(2-g) & **[PERSON]** & **True** & **0.2387** \\ \hline Figure 14(3-a) & An architecture & No face & N/A \\ Figure 14(3-b) & [PERSON] & No face & N/A \\ Figure 14(3-c) & No result & No face & N/A \\ Figure 14(3-d) & No result & No face & N/A \\ Figure 14(3-e) & No result & No face & N/A \\ Figure 14(3-f) & **[PERSON]** & **True** & **0.3157** \\ Figure 14(3-g) & **[PERSON]** & **True** & **0.2467** \\ \hline Figure 14(4-a) & An architecture & No face & N/A \\ Figure 14(4-b) & No result & No face & N/A \\ Figure 14(4-c) & No result & No face & N/A \\ Figure 14(4-d) & No result & No face & N/A \\ Figure 14(4-e) & No result & No face & N/A \\ Figure 14(4-f) & **[PERSON]** & **False** & **0.5538** \\ Figure 14(4-g) & **[PERSON]** & **True** & **0.2938** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Face identification results on two mainstream platforms. (Our results are highlighted in bold. )the reflection layer should be differentiated for different application scenarios, such as the applications of photographer identification of reflection scenes proposed in this paper.