# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 4 Experiment and Result Analysis
### Results on RESIDE Dataset  
In this section, our proposed algorithm MFID-Net is compared with classical single-image dehazing algorithms (DCP, AOD-Net, GCA-Net, GridDehazeNet, FFA-Net) to evaluate their dehazing effects on indoor datasets (IST) and outdoor datasets (OST). Table 2 presents a quantitative comparison of SSIM, PSNR, and rate of improvement-time on IST and OST. It is evident that MFID-Net achieved the best results in terms of both SSIM and PSNR indicators. Moreover, compared to GridDehazeNet and FFA-Net, which are based on multiple stacked attention mechanisms, MFID-Net also demonstrates superiority in computational speed. Figure 7 demonstrates the convergence properties of MFID-Net on the IST and OST datasets, validating the algorithm's stability throughout the training process and unequivocally illustrating its ability to successfully and robustly converge to the anticipated optimal solution. Figures 8 and 9 provide a visual comparison of MFID-Net with other techniques on IST and OST. Although DCP removed most of the haze, the color saturation of its restored images was too high, leading to noticeable distortions. AOD-Net only eliminated superficial fog; the amount of remaining fog residue increased with rising haze concentration. GCA-Net still left a small amount of fog residue in areas with high fog concentration. Despite significant improvements in dehazing by GridDehazeNet and FFA-Net, they could still be enhanced in terms of image detail restoration, and problems like color block loss were common. In contrast to these algorithms, MFID-Net showed excellent performance in removing image haze and restoring image details and color. The images restored by MFID-Net were closer to the original images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**DateSet** & **Evaluation** & **DCP** & **AOD-Net** & **GCA-Net** & **GridDehazeNet** & **FFA-Net** & **MFID-Net** \\ \hline \multirow{2}{*}{IST} & PSNR & 16.86 & 19.26 & 30.23 & 32.16 & 36.13 & 37.42 \\  & SSIM & 0.8601 & 0.8414 & 0.9501 & 0.9836 & 0.9842 & 0.9890 \\ \hline \multirow{2}{*}{OST} & PSNR & 19.37 & 20.17 & 29.76 & 30.86 & 33.38 & 34.21 \\  & SSIM & 0.8431 & 0.8769 & 0.9498 & 0.9752 & 0.9839 & 0.9844 \\ \hline \multicolumn{2}{c}{Rate of improvement-time} & 1 & 97.31\% & 97.39\% & 91.03\% & 94.94\% & 96.68\% \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons for different methods. The Rate of improvement-time indicates the average speed improvement in processing an image of the same size compared to DCP. Figure 7: The convergence characteristics of MFID-Net on the IST and OST datasets. To further validate the superiority of MFID-Net in dehazing large areas of color blocks such as roads and sky, we selected parts of the outdoor dataset for comparison. The dehazing comparison is shown in Figure 10. It is clearly seen that DCP, when handling areas like the sky and road, tends to generate high-saturation images, and sometimes produces black blocks when dealing with large white areas. The images produced by AOD-Net have low contrast in the background. When GCA, GridDehazeNet, and FFA-Net deal with large color block areas such as sky and roads, their restoration of image details is not ideal, often resulting in problems like black blocks and ghosting. In contrast, MFID-Net, when handling large color block areas, is superior to the aforementioned methods in terms of color block areas. Figure 8: Indoor results. (**a**) Hazy, (**b**) DCP, (**c**) AOD-Net, (**d**) GCA-Net, (**e**) GridDehazeNet, (**f**) FFA-Net, (**g**) MFID-Net, (**h**) Clear. Figure 9: Outdoor results. (**a**) Hazy, (**b**) DCP, (**c**) AOD-Net, (**d**) GCA-Net, (**e**) GridDehazeNet, (**f**) FFA-Net, (**g**) MFID-Net, (**h**) Clear. of restoring image color and preserving image details. More importantly, it also reduces the occurrence of undesirable phenomena such as black blocks and ghosting. In the real world, foggy images usually have an uneven distribution of fog density, which adds complexity to the dehazing task of algorithms in real environments.