# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 4 Experiments
### Comparison with the State of the Art  
We compared the results of HarmoDehazeDiffusion with those of eight state-of-the-art methods: DehazeFormer [15], FFA-Net [22], MSBDN [16], DEA-Net [44], UDN [45], Dehamer [14], Grid [46], and the FCDM [47]. To ensure fairness in the experiments, we used the same training data and input sizes for all methods during training. In the evaluation phase, we quantitatively assessed the dehazing performance using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The PSNR measures the pixel-level difference between the dehazed image and the real clear image, while SSIM evaluates image quality in terms of structure, brightness, and contrast. We summarize the quantitative results of all of the methods on the test dataset in Table 1. The results show that our proposed HarmoDehazeDiffusion method outperforms the other learning-based competing methods in both its PSNR and SSIM values. This advantage can be attributed to the fact that the other baseline methods do not adequately consider the specificity of regions of dense haze, leading to their poor performance in complex haze scenarios. For example, these methods do not effectively utilize depth information and long-range features when processing scenes containing dense haze, making it difficult to reconstruct the texture of these areas. In contrast, HarmoDehazeDiffusion introduces depth information into the gradual denoising process and uses multi-scale feature extraction to combine the depth information and long-range features at each step of the network, resulting in stronger robustness when dealing with complex hazy scenes. Moreover, the spatial feature fusion of HarmoDehazeDiffusion effectively combines the shallow texture features with deep lighting features, avoiding the issue of the texture features of regions of dense haze being restored but the lighting in these regions appearing inconsistent with that in the surroundings. In short, our method employs multiple strategies to effectively address texture restoration in dense hazy regions and harmony in the lighting between these regions and their surroundings. From Figure 4, it can be seen that outdoor scenes have significant depth variations, where the depth information plays a crucial role in processing these scenes, as it directly affects the model's understanding of the scene's structure and modeling of the haze distribution. Specifically, although the FCDM method can remove most of the haze from the image, its dehazing results are still not ideal. Visual comparisons show that the FCDM often fails to fully restore clear details in dense hazy regions, resulting in images that remain blurry. Additionally, while the Grid method improves the dehazing effect, it distorts the scene's original colors, causing noticeable color bias issues in the dehazed images, such as an unnatural color in areas of sky. Similarly, the DehazeFormer method, while it improves the dehazing effect to some extent, exhibits the significant loss of texture details in complex scenes, such as the texture of building surfaces or the details of tree branches and leaves becoming blurred. The main reason for the poor performance of these methods is that they ignore the depth information in the image and do not fully consider the differences in the haze distribution across different depth regions. In the indoor scene illustrated in Figure 5, while the variation in depth is less pronounced, it imposes more stringent requirements on the texture details and color consistency of the scene. Although methods such as the FCDM, Grid, and DehazeFormer can effectively remove haze, the FCDM suffers from inadequate color coordination, Grid exhibits color distortions due to its inability to perceive hierarchical information, and DehazeFormer demonstrates a suboptimal dehazing performance in high-luminance regions of images.