# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Transmission Removal Results for General Scenarios  
This sub-section evaluates our transmission removal results in general scenarios, both quantitatively and qualitatively. To be fair, we chose methods that provide testing codes for quantitative comparison and selected those methods that provide training codes for an intuitive comparison. Note that for the methods that have provided the training codes, we also retrained their network with the same dataset that we used to train our network. After training, we conducted tests using both real-world and synthetic images. First, Table 2 lists the quantitative comparison of the proposed method with other methods using three metrics of LPIPS, SSIM, and PSNR, where each metric is averaged on 50 real-world mixture images. For each metric of comparative methods, the number to the left of \"/\" represents the retraining results, and the number to the right represents the results with the originally trained models. Note that \"-\" stands for the methods that do not release the training code. Among all the methods, [10; 11; 14; 15; 16; 17; 20], and Ours(a) have the same input (i.e., a single image circumstance), while [13] and Ours(b) have another input case (i.e., a flash/ambient light image pair as input in addition to the mixture image.) As can be observed in the table, our results perform best among all the comparative methods, and the gains over the state-of-the-art methods are more than 5.356 dB in PSNR, 0.116 in SSIM, and 0.057 in LPIPS. Meanwhile, we found two noteworthy phenomena during our quantitative evaluations: (1) Except for [13], the methods that provide the training code have better metrics after retraining. This is likely due to the fact that different data sets still have a significant impact on the results of the existing reflection removal methods. Since most of the datasets we used are from [13], retraining has no significant effect on this method. (2) Among all methods, the method [15] performed the worst in all metrics, which may be because too much information about the corresponding transmission layer is obtained through the reflection estimation. In order to show the superiority of the proposed method more intuitively, Figure 9 illustrates the LPIPS values on all 50 test images. Note that, in the figure, we mainly compare our method with [13; 14; 16; 17], which have provided the training codes, and the data we use are the results obtained after retraining with these training codes. Our results are consistent with Table 2, showing better perceptual metrics than previous methods on almost every image. Next, Figure 10 shows four examples of our results with the other four comparative methods of [13; 14; 16; 17], where all the images were tested on the real-world dataset. In all experimental results, a simple histogram equalization operation was performed on \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LPIPS (\(\downarrow\))** & **SSIM (\(\uparrow\))** & **PSNR (dB) (\(\uparrow\))** \\ \hline [PERSON] et al. [11] & -/0.512 & -/0.716 & -/21.934 \\ \hline [PERSON] et al. [10]/ & -/0.578 & -/0.723 & -/23.581 \\ [PERSON] et al. [20] \(\ddagger\) & -/0.635 & -/0.420 & -/9.615 \\ \hline [PERSON] et al. [14] \(\ddagger\) & 0.544 & 0.503 & 16.737 \\ \hline [PERSON] et al. [16] & 0.594/0.659 & 0.705/0.643 & 20.785/19.383 \\ \hline [PERSON] et al. [17] & 0.641/0.673 & 0.734/0.625 & 23.657/20.264 \\ \hline Ours(a) & 0.508 & 0.800 & 24.883 \\ \hline [PERSON] et al. [13] & 0.455/0.453 & 0.729/0.735 & 23.583/23.703 \\ \hline Ours(b) & **0.396** & **0.851** & **29.059** \\ \hline \hline \end{tabular} Note: “\(\ddagger\)” stands for that the separation network architecture applied in [20] is employed from [10]; \(\ddagger\)” represents the method [14] as a class of model-driven method that does not have a pre-training problem and therefore will only have one value in each metric; “\(\cdot\)” stands for unavailable due to the absence of released training code.  
\end{table}