# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 4 Experiments and Analysis
### Performance and Discussion  
In the study, PyTorch was applied for developing the deep learning architecture with a central processing unit (CPU) of a 3.00 GHz Intel Core i5-8500 CPU. The GPU processor used a GeForce GTX 1660 Super 6G for training, the batch size was set to 8, and the loss function of Equation (13) was adopted. A learning rate of 0.0001 and 150 epochs were used for the Adam optimizer [30]. A total of 90% of the training dataset was used for training, and 10% was used for verification, which can be used to confirm the loss trend and ensure that the model does not overfit. The image-dehazing CNN results were analyzed and compared using two metrics, namely, _PSNR_ and _SSIM_, introduced in the previous section. Table 6 compares the image quality metrics of the different methods applied to the RESIDE SOTS indoor and outdoor testing datasets. The datasets contain images of various scenes with different haze concentrations. From Table 6, the proposed image-dehazing CNN leads to better results than the prior-based methods and learning-based methods that have been proposed in recent years. Compared to the latest approach, TheiaNet [20], the average _SSIM_ of the indoor dehazing results increased by 5.65%, and the average _PSNR_ increased by 7.73%, whereas the average _SSIM_ of the outdoor dehazing results increased by 2.83%, and the average _PSNR_ increased by 14.09%. Table 7 compares the image quality metrics of the different methods applied to the RESIDE HSTS testing dataset. Compared to TheiaNet [20], the average _SSIM_ of the dehazing results increased by 2.47%, and the average _PSNR_ increased by 8.09%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & \multicolumn{2}{c}{**Indoor**} & \multicolumn{2}{c}{**Outdoor**} \\ \hline
**Method** & _SSIM_ **(\% inc)** & _PSNR_ **(\% inc)** & _SSIM_ **(\% inc)** & _PSNR_ **(\% inc)** \\ \hline DCP [4] & 0.8179 (17.75) & 16.62 (66.79) & 0.8148 (19.49) & 19.13 (52.38) \\ CAP [5] & 0.8364 (15.15) & 19.05 (45.51) & 0.8514 (14.35) & 22.46 (29.79) \\ DehazeNet [9] & 0.8472 (13.68) & 21.14 (31.13) & 0.8630 (12.82) & 22.57 (29.15) \\ AOD-Net [10] & 0.8504 (13.25) & 19.06 (45.44) & 0.8765 (11.07) & 20.29 (43.67) \\ HIDeGAN [17] & 0.8680 (10.96) & 24.71 (12.18) & 0.8780 (10.89) & 25.54 (14.13) \\ ReViewNet [28] & 0.8946 (7.66) & 23.61 (17.41) & 0.9137 (6.56) & 23.64 (23.31) \\ TheiaNet [20] & 0.9116 (5.65) & 25.73 (7.73) & 0.9468 (2.83) & 25.55 (14.09) \\ Proposed Method & 0.9631 (best) & 27.72 (best) & 0.9736 (best) & 29.15 (best) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of dehazing results on RESIDE SOTS dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _SSIM_ & _PSNR_ & **Trainable Parameters** \\ \hline Model 1 & 0.9587 & 25.04 & 123.539 K \\ Model 2 & 0.9602 & 26.01 & 175.107 K \\ Model 3 & 0.9644 & 26.50 & 191.619 K \\ Model 4 & 0.9673 & 27.31 & 196.885 K \\ Model 5 & 0.9718 & 28.64 & 196.885 K \\ Model 6 & 0.9736 & 29.15 & 207.267 K \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of ablation analysis on RESIDE SOTS outdoor dataset. To estimate the average execution time per frame, 500 hazy images with the original resolution of \(620\times 460\) were processed by the trained dehazing CNN. It only took 0.018 s per frame, on average, through the GPU, which fulfills the real-time application requirement, and it took only 0.24 s, on average, through the CPU. Tables 8 and 9 compare the average execution time per frame of the dehazing models by ReViewNet [28] and TheiaNet [20]. Since DehazeNet [9] and AOD-Net [10] did not produce satisfactory dehazing quality, as listed in Table 6, the corresponding average execution times of both methods are not included in Tables 8 and 9. Some comparisons between the dehazing results of the RESIDE SOTS indoor and outdoor testing datasets and ground truth are demonstrated as follows. Figure 8 illustrates the indoor dehazing results. The box indicates the key recovery area where the image is not clear due to the haze.