# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 3 Proposed Method
### Overall Network Architecture  
Figure 4 illustrates an overall schematic diagram of the proposed method, where the reconstruction network is a cascade architecture consisting of a mixture image feature-enhancement network \(\mathbf{Net_{1}}\) and a two-stage reflection separation network with transmission removal \(\mathbf{Net_{2}}\). The specific function models of \(\mathbf{Net_{1}}\) and \(\mathbf{Net_{2}}\) are as follows:  
\[\mathbf{Net_{1}}: \left(\mathbf{I^{\prime}},\mathbf{E_{B}}\right)=G_{1}(\mathbf{I}),\] \[\mathbf{Net_{2}}: \left(\mathbf{\hat{B}_{C}},\mathbf{\hat{B}_{E}}\right)=G_{2-1}( \mathbf{I^{\prime}},\mathbf{E_{B}}), \tag{1}\] \[\hat{f}(\mathbf{R})=G_{2-2}\left(\mathbf{I},\mathbf{\hat{B}_{C}}, \mathbf{\hat{B}_{E}}\right),\]  
where \(\mathbf{I}\) is the shared input image, \(\mathbf{I^{\prime}}\) is the enhanced image, \(\mathbf{E_{B}}\) is the enhanced edge image, \(G_{1}\) is the feature enhanced network, \(G_{2-1}\) and \(G_{2-2}\) are the two-stage extraction networks, \(\mathbf{\hat{B}_{C}}\) and \(\mathbf{\hat{B}_{E}}\) are both predicted transmission features, where the former and latter represent the color distribution and texture distribution of the predicted image, respectively. \(\hat{f}(\mathbf{R})\) is the predicted reflection feature.  
(1) \(\mathbf{Net_{1}}\)  
The attention mechanism focuses on enhancing small-scale features using the global attention mechanism, while extracting gradient profile information about the transmission layer as additional information. We use the Transformer architecture [22] as the global attention mechanism, which has yet to be developed for visual processing applications, with the most significant limitation being the consistent chunking of images when processed as input [23]. This imposes strict requirements on the size of the input. Thus, to meet the requirements of this study, we only use \(\mathbf{Net_{1}}\) for the network of enhanced image features. Secondly, although the mixture image has been greatly changed, the transmission layer in the mixture image still dominates. Hence, the feasibility exists to perform processing directly using means related to edge extraction. At the same time, according to some existing studies, using gradient edge extraction, the underlying information can satisfy the requirements for the gradient edge, and if too much deep-level information is used, this will instead cause information redundancy when regenerating. Therefore, we only refer to the underlying convolutional features in \(\mathbf{Net_{1}}\) for the edge information, and the specific analysis will be described in the ablation experiment.  
(2) \(\mathbf{Net_{2}}\)  
The features obtained in the previous section are developed for further learning, with the ultimate goal of exploring effective ways to achieve the removal of the transmissive layer using both the enhanced features and the gradient profile. Specifically, the paired information obtained in the previous part of the network is closely related to the transmittance layer. Therefore, in order to better consider the visual effect of the predicted reflection layer, we do not use a single step for predicting the reflection layer, but first, perform a prediction operation for the transmission layer, and then use this predicted transmission  
Figure 4: Overall schematic diagram of the proposed method.  
layer information containing edge and color block information as an auxiliary prior for guiding the reflection layer separation.  
Sections 3.2 and 3.3 introduce \(\mathbf{Net_{1}}\) and \(\mathbf{Net_{2}}\) in detail, respectively.