## 4 Results and Analysis
### Restoration Results of Degraded Images on TDIRM  
On the synthetic traffic foggy image test set, quantitative comparisons were conducted between TDIRM and various types of mainstream dehaze algorithms, including the (1) dark channel prior (DCP) algorithm [40] based on physical characteristics. (2) Color attenuation prior (CAP) algorithm [41] based on modeling the relationship between brightness and saturation. (3) The FFA-Net [42] algorithm, which enhances restoration capability through feature attention and multi-scale fusion. (4) The AOD-Net [43] algorithm, which jointly optimizes transmission and atmospheric light end-to-end. (5) The DehazeFormer [44] algorithm, which combines CNN's local feature extraction capability and transformer's global feature extraction capability. (6) The Restormer [45] algorithm is based on channel self-attention and gated convolutional feed-forward networks. (7) The DiffBir [46] algorithm is based on joint training of degradation estimation and diffusion generation. (8) The WeatherDiff [47] algorithm supports multi-weather restoration. As shown in Table 11, comparison models mainly include three categories: physics-based, CNN/transformer-based neural network structures, and diffusion model-based. On the synthetic paired traffic foggy image test set, TDIRM achieved the best results in PSNR and NIQE. But for SSIM, DehazeFormer and Restormer obtained slightly higher results than TDIRM. This demonstrates that TDIRM exhibits superior overall performance in dehaze, particularly showing significant improvement in the naturalness of restored images. While ensuring the fidelity of restored images, TDIRM leverages the powerful implicit prior knowledge of SDM to enhance the realism and quality of images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & Type (mAP50) (\%) & SSD & CenterNet & Faster R-CNN & Mask R-CNN & RetinaNet \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & Clean & 72.0 & 67.1 & 79.7 & 80.6 & 78.6 \\ \cline{2-7}  & Rain & 50.5 & 46.6 & 59.9 & 61.7 & 57.4 \\  & Fog & 47.3 & 44.1 & 54.5 & 60.2 & 57.0 \\  & Frost & 48.5 & 45.3 & 56.4 & 59.7 & 56.8 \\  & Snow & 49.0 & 46.9 & 56.9 & 60.4 & 55.3 \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & Brightness & 53.8 & 52.7 & 64.2 & 66.5 & 63.1 \\  & Dark & 50.1 & 49.4 & 60.4 & 62.7 & 61.4 \\  & Saturate & 54.0 & 53.2 & 65.2 & 67.4 & 63.0 \\ \hline \multirow{8}{*}{\
\begin{tabular}{} \end{tabular} } & GaussianNoise & 48.1 & 45.9 & 56.0 & 58.7 & 56.1 \\  & SpeckleNoise & 46.2 & 44.8 & 54.8 & 58.5 & 55.4 \\  & ZoomBlur & 14.5 & 12.7 & 20.4 & 23.8 & 20.0 \\  & MotionBlur & 46.0 & 43.7 & 54.4 & 57.1 & 53.4 \\ \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & ElasticTransform & 47.1 & 43.5 & 55.1 & 57.6 & 55.9 \\  & JpegCompression & 49.4 & 44.5 & 54.9 & 59.1 & 56.1 \\ \cline{1-1}  & Occlusion & 5.9 & 5.0 & 9.5 & 12.7 & 10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The benchmark results of remaining models. We show robustness metric (mAP50) under each corruption based on the car, truck, and bus classes at severe level. Since TDIRM is designed to efficiently restore degraded images in real traffic scenarios, we focus on validating its restoration performance on real-world degraded traffic images. As shown in Figure 20, the restoration results of several models on real dense fog traffic images are presented. Unlike synthetic image pairs, these real degraded images lack corresponding clear images as reference. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **NIQE** \\ \hline DCP & 21.95 & 0.924 & 4.927 \\ CAP & 23.36 & 0.931 & 4.815 \\ FFA-Net & 29.71 & 0.955 & 4.196 \\ AOD-Net & 22.47 & 0.893 & 4.335 \\ DehazeFormer & 30.57 & 0.976 & 3.973 \\ Restormer & 29.54 & 0.973 & 4.415 \\ DiffBIR & 28.49 & 0.958 & 4.229 \\ WeatherDiff & 26.72 & 0.952 & 4.341 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Quantitative comparison of different dehaze models on paired synthesized traffic foggy images. Figure 19: Class activation mapping of cars.