# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 5 Conclusions and Further Studies  
This paper proposes an efficient RRA-Net to perform single-image dehazing. The contributions of the work are a training-time multi-branch residual attention block, an end-to-end lightweight network structure, and two new loss functions. Multi-branch convolutions in the MRAB are able to deal with non-uniformity of haze. Additionally, due to lightweight design, RRA-Net only has about 0.3M parameters, achieving a good balance between the dehazing performance and model complexity. The experimental results demonstrate that RRA-Net outperforms other SOTA methods on both realistic nonhomogeneous and homogeneous hazy image datasets. With the help of its lightweight network structure, RRA-Net is able to dehaze 1600 \(\times\) 1200 HD images at a speed of 166 fps on a computer with 2 RTX 2080 Ti GPUs, demonstrating its application potential in real-time dehazing. RRA-Net can not only produce high-quality \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Setting**} & \multicolumn{4}{c}{**Module/Loss**} & \multicolumn{3}{c}{**Metric**} \\  & \(\mathbf{BN}\) & \(\mathbf{AM}\) & \(\mathbf{LR}\) & \(\mathbf{L}_{1}\) & \(\mathbf{L_{CA}}\) & \(\mathbf{L_{Laplace}}\) & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** \\ \hline S1 & & & & ✓ & ✓ & ✓ & 18.797 & 0.733 \\ S2 & ✓ & ✓ & & ✓ & ✓ & ✓ & 19.532 & 0.754 \\ RRA-Net & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & 19.813 & 0.765 \\ \hline S3 & ✓ & ✓ & ✓ & ✓ & & & 19.21 & 0.746 \\ S4 & ✓ & ✓ & ✓ & ✓ & ✓ & & 19.34 & 0.741 \\ RRA-Net & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & 19.813 & 0.765 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation studies on main modules of MRAB (top rows) and on two new loss functions (bottom rows). \(BN\), \(AM\), and \(LR\) stand for batch normalization, attention module (spatial attention and channel attention), and local residual connection in MRAB, respectively. Best results are underlined. \begin{table}
\begin{tabular}{l l} \hline \hline
**BN Strategy** & **PSNR** & **SSIM** \\ \hline Individual BN on each branch & 18.627 & 0.688 \\ A single BN after \\ multi-branch & 19.813 & 0.765 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study on different BN strategies. Best results are underlined. clean images for better human perception but also contribute to the success of subsequent real-world vision tasks in hazy weather, such as object detection and autonomous driving. Despite the superiority of RRA-Net, it is CNN-based and lacks ability for global modeling. As mentioned earlier, recently, there have been some efforts to take advantage of both visual transformers and CNNs. However, such hybrid networks usually have a much higher complexity. Thus, it is worth investigating how to bestow RRA-Net with the merits of transformers while keeping the network as efficient as possible at the same time. Conceptualization, E.C. and T.Y. ; methodology, E.C. and T.Y. ; software, T.Y. and J.J.; validation, J.J. and L.T. ; formal analysis, Q.Y. and L.T. ; investigation, E.C. and T.Y. ; resources, Q.Y. ; data curation, T.Y. ; writing--original draft preparation, T.Y., J.J. and Q.Y. ; project administration, Q.Y. ; funding acquisition, E.C. and Q.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Fujian Province of China under grant no. 2021J01867, the Scientific Research Foundation of Jimei University under grant no. ZQ2018012, and the Xiamen Municipal Bureau of Ocean Development  
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