# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Experiment Preparation
#### 4.1.3 Datasets  
Existing Dataset  
We used two existing datasets in our evaluation. 1. The reflection removal method proposed in [PERSON] et al. [41] provides a reasonable way to obtain the ground truth for the reflection component image. In detail, by putting a piece of black cloth behind the glass, only the camera can capture the reflection light reflected by the glass. The \(\text{SIR}^{2}\) dataset (Downloaded from [https://sir2 data.github.io](https://sir2 data.github.io), accessed on 29 July 2022) in [41] has 500 real-world triplets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Publication** & **Main Technical Route** & **TSCR** & **TRCR** \\ \hline [PERSON] et al. [11] & CVPR2019 & Multi-scale edge guidance model & Yes & No \\ [PERSON] et al. [10] & CVPR2018 & Dilated convolution model and exclusion loss & Yes & No \\ [PERSON] et al. [15] & ECCV2019 & Bi-directional estimation model & Yes & No \\ [PERSON] et al. [14] & CVPR2015 & Distribution-based traditional model & Yes & N/A \\ [PERSON] et al. [13] & CVPR2021 & Free reflection cue guidance model & Yes & Yes \\ [PERSON] et al. [16] & WACV2021 & Edge guidance and recurrent decomposition model & Yes & Yes \\ [PERSON] et al. [17] & CVPR2020 & Cascade network with LSTM module and reconstruction loss & Yes & Yes \\ [PERSON] et al. [20] & CVPR2020 & Dilated convolution model, U-Net, and shift-invariant loss & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The baseline methods to be compared in this paper. 2. Recently, [PERSON] et al. [13] proposed a method to obtain a pure flash transmission layer with no reflection. The article minimizes the effect of the reflection layer by combining the image before and after the flash. After preprocessing the image, the triplet image can be obtained as the dataset (Downloaded from [[https://hkustconnect-my.sharepoint.com/personal/cleiaa_connect_ust_hk/Documents/Projects/cvpr2021-flash-rr/data.zip?ga=1](https://hkustconnect-my.sharepoint.com/personal/cleiaa_connect_ust_hk/Documents/Projects/cvpr2021-flash-rr/data.zip?ga=1)]([https://hkustconnect-my.sharepoint.com/personal/cleiaa_connect_ust_hk/Documents/Projects/cvpr2021-flash-rr/data.zip?ga=1](https://hkustconnect-my.sharepoint.com/personal/cleiaa_connect_ust_hk/Documents/Projects/cvpr2021-flash-rr/data.zip?ga=1)), accessed on 29 July 2022). Their dataset consists of 4056 triplet images for training and 1012 triplet images for validation, both synthetic and real-world. Note that this dataset contains 35 triplet images from [41], so we eliminate them to obtain 4021 training triplet images. Some examples from the existing datasets are shown in Figure 7.  
Synthetic Data Generation  
Reflection layers in real-world datasets are usually relatively fixed scenes, while there is no relevant dataset for moving objects. The existing method of obtaining a pure reflection layer in a real-world scene is to use a black screen to block the light transmitted from the background image. The main difficulty is that we must simultaneously obtain the image pair of pure reflection images and mixture images with moving objects. The specific operation usually requires fixing the reflection image at the pixel level accuracy, but this is not easy to achieve ultimately. Therefore, we chose a synthetic method that can obtain the pure reflection layer and satisfy the realistic visual effect at the same time as the previous reflection removal experiments. Figure 8 shows the example results of two representative synthesis methods proposed in [15; 32]. Although more mixture images closer to the real-world glass effect can be generated by [32], the coefficient matrix involved in the experiment confuses the relationship between the two layers, making it difficult to obtain pure reflection images. Considering the above issue, we adopted the method [15] to synthesize the data in the literature [32] and obtained 4000 sets of data.