# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 4 Experiments
### Comparison with the State of the Art
In the real-world scene shown in Figure 6 (the quantitative results are summarized in Table 2), since the dataset is directly derived from the real world, it provides richer visual information and more accurately reflects the performance of the algorithms in practical environments. The results show that the FCDM, Grid, and DehazeFormer methods each have certain limitations. The FCDM method is prone to color distortions due to its insufficient ability to perceive the features, which leads to biased estimations of the atmospheric light. Grid, on the other hand, suffers from artifacts in the edge regions because it fails to effectively integrate the texture features. As for DehazeFormer, it performs poorly in dense and non-uniform haze conditions with complex visual information. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**SOTS-Indoor**} & \multicolumn{2}{c}{**SOTS-Outdoor**} & \multicolumn{2}{c}{**HAZE4K**} \\  & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** \\ \hline Grid [15] & 0.945 & 30.64 & 0.895 & 28.39 & 0.835 & 22.72 \\ FFA-Net [22] & 0.961 & 35.20 & 0.955 & 33.00 & 0.938 & 26.36 \\ MSBDN [16] & 0.959 & 31.62 & 0.954 & 34.26 & 0.846 & 22.40 \\ UDN [45] & 0.961 & 37.61 & 0.952 & 34.34 & 0.860 & 23.14 \\ Dehamer [14] & 0.970 & 35.60 & 0.951 & 34.63 & 0.871 & 23.43 \\ DehazeFormer [46] & 0.966 & 37.85 & 0.950 & 35.32 & 0.966 & 33.61 \\ DEA-Net [44] & 0.969 & 39.00 & 0.957 & 36.02 & 0.976 & 32.64 \\ FCDM [47] & 0.971 & 39.40 & 0.961 & 36.12 & 0.978 & 34.08 \\ Ours & **0.983** & **39.81** & **0.973** & **37.23** & **0.981** & **36.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation of our method and other image dehazing methods. In contrast, our proposed HarmoDehazeDiffusion method, through the introduction of a depth-aware mechanism, spatial feature fusion, and multi-scale feature extraction, can more accurately capture various information in the scene and adaptively process the haze removal based on the characteristics of the haze distribution in different regions. This enables our method to effectively avoid color distortion issues in the results, such as preserving the natural color transitions in areas of sky while restoring the detail information for distant views. Additionally, HarmoDehazeDiffusion maintains a balance between the texture details and lighting consistency better, resulting in detailed and harmonious outcomes. These improvements are not only reflected in the quantitative metrics but are also fully validated by the visual comparisons, further demonstrating the superiority and robustness of our method in handling complex hazy scenes. Although our method has achieved some positive results, it cannot reach an optimized real-time elimination speed due to the various computational strategies in its design. Specifically, we processed a 256 \(\times\) 256 image on a 3090 Ti, with a processing time of 0.256 s and occupying 1547 MB of graphic memory. Figure 4: Comparison of dehazing effects on images with significant changes in depth (outdoor). Figure 5: Comparison of dehazing effects on images with small changes in depth (indoor). Figure 6: Comparison of dehazing effects on real images.