# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 1 Introduction  
An essential objective of computer vision is to extract information within an image to enhance the perception of the world while satisfying the visual effect [1; 2; 3; 4; 5; 6]. According to the effect of the glass on the light, there is a significant difference in characteristic information in the obtained images that are captured through a glass [7]. In natural scenes, the mixture image with reflection usually contains two kinds of information: the dominant transmission layer and the non-dominant reflection layer. Most existing methods treat the reflection layer as noise, and their main goal is to recover the transmission layer from the mixture image. However, the reflection layer is as important as the transmission layer in many practical applications. The glass reflection information can be used to assist in scene and photographer identification. For instance, in some subway stations, museums, or jewelry stores with glass scenes, as shown in Figure 0(a), the reflection images containing the photographer's facial features are more likely to be critical clues for surveillance investigation. This paper focuses on recovering a clear reflection image via transmission removal from the mixture image. Considering the influence of glass and combined with light analysis in the mixture scene, a mixture image can be regarded as a superposition of the transmission layer and reflection layer as  
\[\mathbf{I}=g(\mathbf{B})+f(\mathbf{R})=g(\mathbf{B_{C}},\mathbf{B_{E}},\ldots)+ f(\mathbf{R}), \tag{1}\]  
where \(\mathbf{I}\) represents a mixture image; \(g(\cdot)\) and \(f(\cdot)\) represent the changes of the object's light before it reaches the sensor, and are caused by many complicated factors and difficult to be expressed in a specific mathematical formula; \(\mathbf{B}\) represents the light from the actual background image behind the glass, which contains the components such as the color distribution \(\mathbf{B_{C}}\) and the edge information \(\mathbf{B_{E}}\), and \(\mathbf{R}\) represents the light from the reflection scene image. Figure 1b shows the imaging principle under an ambient light environment, where according to the object layer for reconstruction, i.e., \(\mathbf{B}\) or \(\mathbf{R}\), the enhancement problem of the mixture image with glass scene can be divided into two aspects: reflection removal and transmission removal, as shown in Figure 1c. On the one hand, due to the high transmittance rate of glass, in most occasions, \(g(\mathbf{B})\) is remarkably close to \(\mathbf{B}\), which makes the transmission component dominate the mixture image \(\mathbf{I}\). In our method, we no longer distinguish between \(g(\mathbf{B})\) and \(\mathbf{B}\), i.e., \(g(\mathbf{B}))=g(\mathbf{B_{C}},\mathbf{B_{E}},\ldots)\approx\mathbf{B}=( \mathbf{B_{C}},\mathbf{B_{E}},\ldots)\). On the other hand, most of the reflection light \(\mathbf{R}\) from the reflection scenes will pass through the glass, and only a tiny amount will be reflected by the glass and captured by the camera, which leads to \(f(\mathbf{R})<\mathbf{R}\). In general, \(f(\mathbf{R})\) is much less than \(g(\mathbf{B})\). Since the extraction of \(f(\mathbf{R})\) is a regenerative process, we need to focus on generating results that express more detailed information, including content and style, for further analysis. In general, the content retains more distinct levels of the feature map than the style. At the end of the generator network, we add a super-resolution (SR) module to optimize and reconstruct high-quality details through a neural network via sampling from the traditional method. Incorporating the above-mentioned motivations, in this paper, we propose a unified framework based on the generative adversarial network (GAN) [8] to remove the dominant transmission component from a mixture image via a comprehensive constraint.