# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 2 Related Works
### Transmission Removal  
Compared with the transmission restoration after removing the reflection, transmission removal pays more attention to exploring the undetectable features in the mixture image. In the traditional methods, exploring the differences between the two layers becomes a prerequisite for implementing the removal of one layer. For example, the information on the reflection layer is highly blurred compared to the transmission layer. [PERSON] and [PERSON] [24] used the distribution to find pixel-level boundaries distinguishing the two layers. With the introduction of DL methods in recent years, relatively satisfactory results have been obtained in separating mixture images by learning features actively. However, this often requires complete prior knowledge as a guide. Based on the difference between the two layers in the gradient domain, most experiments pay more attention to using a gradient. [PERSON] et al. [10] first used the gradients of two layers simultaneously and used edge information as a prior. Similarly, [PERSON] et al. [11; 12] and [PERSON] et al. [16] used a predicted network to take the edge information as a prior. [PERSON] et al. [17] proposed a cascaded network that the two layers can be mutually prior to improve the quality of prediction. [PERSON] et al. [13] used the characteristic that the reflection layer only exists in the mixture image, then used the reflection-free flash-only cues as prior knowledge to guide the separation.  
Compared with the estimation of the reflection layer as a byproduct in the above methods, [PERSON] et al. [20] first raised the problem of reflection separation strictly. Specifically, their whole framework comprises two networks: the separation and enhancement networks. The separation network follows the framework of literature [25], and the main contribution of [20] focuses on improving the visual effect of the separated images by adding a shift-invariant loss and increasing the reflection image brightness via the corresponding ambient light environment image. However, the ground truth information should be retained as much as possible for the estimated reflection layer while meeting the visual requirements. In addition, we also need to consider the scenarios in which the corresponding ambient light environment image is difficult to obtain. In addition to the method [20], the most related one is proposed by [PERSON] et al. [26]. However, the transmission in the mixture image explored by this method is not the dominant image, which is not in line with the actual situation and is not suitable for our discussion. Moreover, some methods [27; 28] extracted facial features of bystanders from corneal reflection and put forward the matching theory, which makes it have application prospect in the criminal investigation. [PERSON] et al. [29] also proposed another method to locate the objects from the reflections. [PERSON] et al. [30; 31] estimated lighting from corneal reflections for the relighting applications.  
In general, in addition to [20], transmission removal has not received widespread attention; there is still room for further research on obtaining more accurate reflection layer images. At the same time, targeted network design is needed for different application scenarios, such as photographer identification.