# Single Image Reflection Removal Based on Residual Attention Mechanism
## 5 Conclusions  
In this study, we proposed an RABRRN based on a residual attention mechanism for single image reflection removal. It is a dual-streamline architecture composed of one encoder and two decoders with identical structures, with two branches used to predict the transmission layer and the reflection layer, respectively. To improve the quality of predictions, we introduced a residual attention module in the encoder, which brings a superior removal effect as shown from experimental data. For the network model training, we combined pixel loss and structural similarity loss in order to produce results consistent with human perception. Furthermore, we established a reflection image dataset named SCAU-RID to study image reflection removal. By comparing our method with the state-of-art, the quantitative and qualitative results revealed that the single image reflection removal method proposed in this paper significantly improved the quality of restored images while reducing the number of parameters.  
There is a lack of a general framework in the field of image restoration, and the existing models are developed and designed for specific tasks. In the near future, we aim to apply the improved network to other image processing tasks such as rain removal, haze removal, and shadow removal. Although we have achieved desirable results in our experiment, it seems that we could not achieve good results for strong reflections over large areas, and we will carry out more research on eliminating large and strong reflections afterwards.  
Conceptualization, X.L; Methodology, Y.G. and W.L.; Software, W.L.; Validation, Y.G. and W.L.; Formal analysis, Y.G.; Resources, X.L.; Writing--original draft, W.L.; Writing--review and editing, Y.G.; Visualization, W.L.; Supervision, X.L.; Project administration, Q.H.; Funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Program of Guangzhou (201902010081) and the National Natural Science Foundation of China (61872152).  
Not applicable.  
The address of the SCAU-RID dataset we collected is: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) RAmend-MjokjNANA4D2 MymA]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) RAmend-MjokjNANA4D2 MymA) (accessed on 24 January 2023), password: SCAU.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **LSME** \\ \hline Del ResCbamBlock & 21.578 & 0.816 & 0.032 \\ ResCbamBlock \(\rightarrow\) CBAM & 23.102 & 0.852 & 0.027 \\ \(\mathcal{L}_{\text{mix}}\rightarrow\mathcal{L}_{\text{pixel}}\) & 23.493 & 0.839 & 0.24 \\ Complete & 24.284 & 0.897 & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A quantitative comparison of various network architectures and loss function approaches was performed. The best outcomes are highlighted in red, while the second best results are highlighted in blue.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameter** & **PSNR** & **SSIM** \\ \hline RmNet [25] & 65.443M & 21.393 & 0.779 \\ IBLCN [26] & 21.608M & 23.518 & 0.876 \\ Ours & 18.524M & 23.787 & 0.885 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of parameter quantity between our method, RmNet [25], and IBLCN [26], indicating that our method has fewer parameters. The best outcomes are highlighted in red, while the second best results are highlighted in blue.