# Single Image Reflection Removal Based on Residual Attention Mechanism
## 4 Experiments
### Qualitative Evaluation  
To further compare different algorithms through visual evaluation, we present some test results in Figures 7 and 8, where each column represents the input of the image, the result of the method comparison, and the ground truth, respectively. The first and second rows in Figure 7 are from the Nature dataset, the third to fifth rows are from ERRNet-50, and the last two rows are in our collected SCAU-RID dataset. The seven types of pictures are toys, indoor potted plants, tripods, houses, pavilions, trees, and outdoor potted plants. The three rows in Figure 8 are from three sub-datasets of the SIR\({}^{2}\) dataset using pictures of bridges, stationery, and toys. To make the evaluation more intuitive, some local patches with strong reflection signals are marked with yellow rectangles in the input image. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Index**} & \multicolumn{5}{c}{**Methods**} \\ \cline{3-6}  & & **RmNet [25]** & **Zhang [1]** & **ERRNet [28]** & **IBLCN [26]** & **Ours** \\ \hline \multirow{3}{*}{Nature [28]} & PSNR & 20.525 & 22.221 & 21.351 & 23.422 & 24.284 \\  & SSIM & 0.785 & 0.812 & 0.881 & 0.893 & 0.897 \\  & LMSE & 0.032 & 0.025 & 0.023 & 0.014 & 0.021 \\ \hline \multirow{3}{*}{Object [39]} & PSNR & 21.347 & 22.032 & 23.565 & 23.375 & 23.622 \\  & SSIM & 0.772 & 0.802 & 0.874 & 0.868 & 0.859 \\  & LMSE & 0.031 & 0.030 & 0.023 & 0.019 & 0.021 \\ \hline \multirow{3}{*}{Postcard [39]} & PSNR & 22.125 & 21.415 & 23.637 & 23.601 & 23.521 \\  & SSIM & 0.847 & 0.797 & 0.862 & 0.876 & 0.871 \\  & LMSE & 0.027 & 0.024 & 0.017 & 0.014 & 0.019 \\ \hline \multirow{3}{*}{Wild [39]} & PSNR & 21.576 & 21.051 & 23.153 & 23.675 & 23.721 \\  & SSIM & 0.794 & 0.820 & 0.862 & 0.869 & 0.883 \\  & LMSE & 0.029 & 0.027 & 0.025 & 0.018 & 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A quantitative comparison of three real-world benchmark datasets and three alternative techniques. The top result are highlighted in red, while the second best results are highlighted in blue. Figure 6: Data visualization of evaluation metrics PSNR and SSIM for qualitative comparisons, where the x-axis displays various methods and the y-axis reflects the values, and the different colored bars in the same method represent the results on different datasets. From the first 2 rows in Figure 7, it can be seen that our method had a good performance in an indoor environment, and the reflection created by incandescent lamps in indoor potted plants was well removed. RmNet performed poorly, even worse than the input results. [PERSON]'s method darkened the restored background as a whole. As seen in the third and fifth rows, none of the methods for comparison can completely remove large-area reflections and strong reflections. IBCLN performed the best on the third row but suffered from color distortion and significant reflection artifacts. By contrast, our method was effective, although it enlarged the original black spots. The reflection of houses in the fourth row was not very strong, and both our method and IBLCN obtained a near-perfect removal effect. For large-area reflections from pavilions, only ERRNet could achieve desirable outcomes. At the same time, in the last two rows in Figure 7, we also listed the test results  
Figure 7: Qualitative comparison between the proposed method and four state-of-the-art techniques. The images are obtained from ‘Nature’ (Rows 1–2), ‘ERRNet-50’ (Rows 3–5), and our SCAU-RID (Rows 6–7). In particular, the first column represents the input image, the last column represents the ground truth image without reflections, and the other columns represent the output corresponding to the comparison method. Column 2 from RmNet [25], column 3 from Zhang [1], column 4 from ERRNet [28], column 5 from IBLCN [26] and column 6 from ours work. on our SCAU-RID dataset.