# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 4 Experimental Results
### Comparison of Results  
In this section, we compare the final generated results with related reflection extraction experiments. The main image measures we use are the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual perceptual similarity (LPIPS). In order to evaluate the performance of the scheme unbiasedly, we compare it with some cutting-edge studies on reflection layer separation.  
Figure 9 presents a comparison of the final generated results, where (a) represents the input image; (b) represents the real scene image of the reflection layer; (c-f) are the images of [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [30], and [PERSON] et al. [9]'s results; and (g) is our results. It can be seen that our experiments were able to achieve the elimination effect as much as possible for the issue of transmission removal from glass mixture images  
Figure 8: Example of the dataset.  
compared to the other comparison experiments, and it can be seen from the comparison experiments that the transmission layer was still difficult remove in the final generated results. The reason for this was that the differences between the two image layers need to be better understood. In order to use the average results obtained from 50 sets of comparison images, we used three evaluation image quality metrics to measure the final generated results in the comparison experiments; the results are listed in Table 1.  
Note that we adopted the recently proposed learned perceptual metric (LPIPS) [31] as an error metric. This measures perceptual image similarity using a pretrained deep network. This metric learns the reverse mapping of generated images to ground truth and forces the generator to reconstruct the reverse mapping of authentic images from fake images and to prioritize the perceived similarity between them. LPIPS is more consistent with human perception than the traditional methods (such as L2/PSNR, SSIM, and FSIM). The lower the LPIPS value, the more similar the two images; otherwise, the greater the difference. Without loss of generality, we also chose SSIM and PSNR as metrics to compare references. As can be observed in the table, our results showed gains of 0.46 dB in PSNR, 0.016 in SSIM, and 0.057 in LPIPS compared to the related studies, and an improvement was achieved in all three metrics.  
Figure 10 shows an LPIPS value line chart for 50 test image sets, and although there are some image groups with higher values, our results were generally more stable and relatively low.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **LPIPS \(\downarrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** \\ \hline [PERSON] et al. [28] & 0.663 & 0.517 & 17.820 \\ [PERSON] et al. [29] & 0.735 & 0.566 & 19.837 \\ [PERSON] et al. [30] & 0.772 & 0.479 & 10.122 \\ [PERSON] et al. [9] & 0.674 & 0.718 & 21.883 \\ Ours & **0.606** & **0.734** & **22.343** \\ \hline \end{tabular}
\end{table}
Table 1: Statistical comparison of quality indicators (best results are bolded).  
Figure 9: Comparison of the final transmission removal results. (**a**) represents the input image; (**b**) represents the real scene image of the reflection layer; (**c**–**f**) are the images of [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [30], and [PERSON] et al. [9]’s results; and (**g**) is our results.