# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 4 Results
### Results of Comparative Experiments  
As shown in Table 1, our RRA-Net outperforms all of the listed SOTA methods on both datasets in terms of the PSNR and SSIM metrics. For the NH-Haze (2021) dataset with nonhomogeneous haze scenes, RRA-Net has a 0.5 dB lead in terms of the PSNR over the second-best method. Additionally, note that RRA-Net works well on both nonhomogeneous hazy images and homogeneous dense hazy images. For the Dense-Haze dataset, RRA-Net achieves a PSNR of 15.78 dB, exceeding the second-best method by 0.37 dB.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**NH-Haze (2021)**} & \multicolumn{2}{c}{**Dense-Haze**} & \multirow{2}{*}{**Param**} & \multicolumn{2}{c}{**Runtime (fps)**} \\  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **\#1600 \(\times\) 1200** \\ \hline DCP [9] (TPAMI\({}^{\intercal}\)10) & 10.57 & 0.52 & 10.06 & 0.3856 & - & - \\ AOD-Net [25] (TIP\({}^{\intercal}\)17) & 14.104 & 0.552 & 13.34 & 0.4244 & 0.002M & 2598.3 \\ FFA-Net [13] (AAAI\({}^{\intercal}\)20) & 19.1 & 0.748 & 14.31 & 0.4797 & 4.68M & 1.28 \\ MSBDN [14] (CVPR\({}^{\intercal}\)20) & **19.31** & **0.759** & **15.41** & 0.4858 & 31.35M & 36.84 \\ DMPHN [15] (CVPR\({}^{\prime}\)20) & 18.184 & 0.745 & 14.01 & 0.4436 & 5.424M & 135.46 \\ TBNN [19] (CVPR\({}^{\intercal}\)21) & 15.622 1 & 0.707 & 14.60 & 0.4829 & 50.40M & - 1 \\ Dehamer [20] (CVPR\({}^{\intercal}\)22) & 13.940 1 & 0.646 & 14.63 & **0.4996** & 132.4M & - 1 \\ \hline RRA-Net (Ours) & 19.813 & 0.765 & 15.78 & 0.5154 & 0.3M & 166.11 \\ \hline \hline \end{tabular}  
* For fair comparisons, we conducted the tests for all the methods on the same computer with 2 RTX 2080 Ti GPUs by passing whole images into these models. For TBNN and Dehamer models, GPU “out of memory” issue was reported during the tests run on this specific computer since they have much higher model complexities. Thus, we used a patching strategy to quantitatively evaluate these two models. Note that this strategy may incur slight losses of performance. Accordingly, the runtimes of these two models are not reported.  
\end{table}
Table 1: Quantitative comparisons with SOTA methods on the realistic datasets. Best results are underlined. The second-best results are in **Bold**.  
Visual comparisons of the recovered results on nonhomogeneous hazy scenes are given in Figures 4 and 5. It can be seen that RRA-Net produces results with less residual haze and preserves more image details and contrast. For example, in Figure 4, the results of the FFA-Net and TBNN methods still have some residual haze; the result of MSBDN loses pixel color saturation to some extent and has a whitish appreance, while the result of DMPHN introduces visible artifacts in the sky area. In addition, the Dehamer method cannot effectively remove the nonhomogeneous haze from either sample given in Figures 4 and 5.  
Figure 4: Qualitative comparison of dehazing results on one nonhomogeneous hazy image from NH-Haze (2021) dataset.  
A visual comparison of the homogeneous dense hazy scene is given in Figure 6. It can be seen that in such a case, RRA-Net is also capable of producing a result with less residual haze.  
Apart from having better dehazing results, RRA-Net is designed to be lightweight from the beginning. Under the same configuration, it has only about 0.3M parameters and runs at 166 FPS to dehaze a 1600 \(\times\) 1200 image on RTX 2080 Ti GPU. As can be seen from Table 1, RRA-Net runs faster than the other methods, except for AOD-Net, which falls behind in terms of the PSNR and SSIM metrics. The speed comparison is conducted under the same hardware setup, demonstrating the superior efficiency of the proposed network.  
Figure 5: Qualitative comparison of dehazing results on another nonhomogeneous hazy image from NH-Haze (2021) dataset.