# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 3 Proposed Method
### Network Architecture  
Overall, we build the model based on the GAN architecture. The related information of transmission layer **B** and the mixture image **I** are used as input. By referring to the existing encoder-decoder architecture of general image-to-image translation methods [10; 11; 17; 32], our generator also uses the U-Net as the backbone, combined with some image progressing methods, such as SR, image transfer, and image reconstruction. In addition, we use PatchGAN to replace the traditional discriminator network.  
The reflection layer generated by the generator network can be formulated as follows:  
\[\begin{split}\text{Case1}:&\hat{f}(\textbf{R})= \text{G}\left(\textbf{I},\hat{\textbf{B}}_{\textbf{E}}\right),\\ \text{Case2}:&\hat{f}(\textbf{R})=\text{G}\left( \textbf{I},\hat{\textbf{B}}_{\textbf{C}},\hat{\textbf{B}}_{\textbf{E}}\right),\end{split} \tag{2}\]  
where \(\text{G}(\cdot)\) represents the generator network. We intend to retain as much information as possible about reflection in the mixture image. The ordinary generator usually generates some edge information during image-to-image translation. Considering the practical applications, we lack information about the estimated results, so we divide the different priors into two cases, one with only the estimated edge \(\hat{\textbf{B}}_{\textbf{E}}\) of the transmission **B** as a prior  
Figure 2: Flow diagram of the proposed method. The inputs contain two cases: (1) the estimated edge \(\hat{\textbf{B}}_{\textbf{E}}\) and the mixture image **I** and (2) the estimated \(\hat{\textbf{B}}_{\textbf{E}}\), \(\hat{\textbf{B}}_{\textbf{C}}\), and the mixture image **I**.  
and one with the predicted transmisson as a prior, which contains color distribution \(\hat{\mathbf{B}}_{\mathbf{C}}\) and edge information \(\hat{\mathbf{B}}_{\mathbf{E}}\). Unlike traditional U-net, we use Resnet to replace the previous VGG network in obtaining the feature map. Its residual structure [33] will help us retain global information from higher levels. In addition, we re-select the feature map after feature extraction by adding the squeeze-and-excitation (SE) and SR modules to enhance the expression of the network in the learning progress.  
In the encoder process, the convolutional layers in U-Net only compute the local image features, while the features like the average intensity affecting the overall visibility are not considered in [20]. This is very shallow for the information we can extract about the reflection from the mixture image. After extracting the features, we add the SE module to solve this problem. We add attention mechanisms to optimize what we learn and suppress unimportant modules. After the feature map is processed and all features are extracted, the image results are not predicted solely based on the local features.  
Meanwhile, because the estimated image is usually blurred after the decoder process, the generated \(\hat{f}(\mathbf{R})\) is blurry when the edge of \(f(\mathbf{R})\) is close to that of \(\mathbf{I}\). SR is a process of restoring a high-resolution image from a given low-resolution image [34,35], which is a classic application of computer vision. Specifically, the enhancement of the correlation in the image via adding bicubic interpolation, proposed by [PERSON] et al. [36], can be used to improve the resolution of the restored image and improve the visual effect. Following [36], we use the idea of SR and add an SR module with three convolutional layers at the end of the generator. By convolution of different sizes, better compression effects are achieved within the region of the size range, resulting in higher resolution.