# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 5 Conclusions  
In this paper, we innovatively propose the HarmoDenzeDiffusion framework specifically designed for haze elimination in dense hazy scenes. The proposed method first utilizes a depth map to gradually guide the denoising process of the diffusion model. Subsequently, we combine multi-scale feature extraction with a spatial fusion module to effectively solve the problems of the loss of long-distance features and the lighting consistency between dehazed areas and the surrounding areas in haze removal. Extensive experimental verification has shown that our solution is effective and superior in scenarios with dense haze, particularly demonstrating unique advantages in maintaining overall consistency in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** \\ \hline Grid [15] & 0.472 & 11.38 \\ FFA-Net [22] & 0.487 & 11.68 \\ MSBDN [16] & 0.475 & 11.52 \\ UDN [45] & 0.480 & 11.61 \\ Dehamer [14] & 0.485 & 11.71 \\ DehazeFormer [46] & 0.470 & 11.35 \\ DEA-Net [44] & 0.491 & 11.80 \\ FCDM [47] & 0.496 & 11.85 \\ Ours & **0.501** & **11.92** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results for real images.  
\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}{**HAZ64K**} \\  & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** \\ \hline _Ours w/o depth_ & 0.960 & 38.91 & 0.941 & 35.81 & 0.959 & 33.71 \\ _Ours w/o multi – scale FE_ & 0.969 & 39.38 & 0.959 & 36.09 & 0.976 & 34.05 \\ _Ours w/o spatial fusion_ & 0.973 & 39.60 & 0.965 & 36.15 & 0.978 & 34.10 \\ _Ours(complete model)_ & **0.983** & **39.81** & **0.973** & **37.23** & **0.981** & **36.61** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of the ablation study.  
lighting. The limitation of our method is its slow inference speed. Overall, our research proposes a novel approach to considering dehazing tasks from the perspective of overall consistency in image lighting, which can prevent the dehazing results from becoming nottled. In the future, we will consider streamlining the network architecture to achieve real-time video dehazing.  
Conceptualization: J.Y. Methodology: J.Y. and S.D. Software: J.Y. and S.D. Validation: J.Y., S.D., L.Y. and H.Y. Formal analysis: J.Y. Investigation: J.Y. Resources: J.Y. Data curation: J.Y. Writing--original draft preparation: J.Y. Writing--review and editing: J.Y., S.D., and H.Y. Visualization: J.Y. Supervision: [PERSON]. Project administration: H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are from public datasets. These data were derived from the following resources available in the public domain: SOTS (DOI: 10.1109/TIP.2018.2867951), [[https://sites.google.com/view/reside-dehaze-datasets](https://sites.google.com/view/reside-dehaze-datasets)]([https://sites.google.com/view/reside-dehaze-datasets](https://sites.google.com/view/reside-dehaze-datasets)) (2018), and Haze4K (DOI: 10.48550/arXiv.2108.02934), [[https://github.com/liuye123321/DMT-Net](https://github.com/liuye123321/DMT-Net)]([https://github.com/liuye123321/DMT-Net](https://github.com/liuye123321/DMT-Net)) (2018).  
The authors declare no conflicts of interest.