# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal  
[PERSON]  
1 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (J.Y. ); [EMAIL_ADDRESS] (S.D)1 School of Electrical Engineering, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (J.Y. ); [EMAIL_ADDRESS] (S.D)1 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (J.Y. ); [EMAIL_ADDRESS] (S.D)1 School of Electrical Engineering, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (J.Y. ); [EMAIL_ADDRESS] (S.D)1 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (J.Y. ); [EMAIL_ADDRESS] (S.D)1 School of Electrical Engineering, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (S.D)1 School of Electrical Engineering, Guangxi University, Nanning 530004, China; yhy@st.  
to hazy images often leads to incomplete haze removal and the generation of visually incoherent artifacts. Recent dehazing algorithms have predominantly adopted learning-based approaches to address the dehazing challenge. Despite some of the success achieved by these solutions, they have generally failed to consider a crucial aspect: the dense regions of haze in images. Dense regions of haze are characterized by complete blurring of their inherent textures and the alteration of their original illumination. This implies that restoring these regions requires a method that simultaneously integrates features from different parts of the image to construct the texture of these dense regions of haze and harmonizes the illumination of dense haze regions with that of the surrounding areas by leveraging the overall lighting information for the image. Based on an analysis of the existing limitations, we propose a network framework called HarmoDehazeDiffusion, which incorporates depth information and considers both long-range features and spatial features to tackle the texture and illumination consistency issues in dense haze regions. Given the inseparable relationship between dense haze and depth, our method continuously introduces depth information during the network's iterative denoising process to enhance the network's perception of depth. Subsequently, to counteract the localized homogeneity of dense haze, our multi-scale feature extraction layer captures the non-local dependencies via dilated convolutions, enabling coherent texture reconstruction. Finally, we employ a spatial fusion strategy that integrates shallow texture features with deep semantic features. A learnable fusion gate dynamically combines these features, thereby ensuring harmonious illumination between the restored dense haze regions and the surrounding areas. Our primary contributions are summarized as follows:  
* We propose a dehazing model based on a diffusion model, named HarmoDehazeDiffusion. By introducing a novel Depth Fusion Diffusion Model, the network achieves a satisfactory dehazing performance even when dealing with dense haze images. * We present a new spatial feature fusion module that enables the network to integrate multi-level information, thereby maintaining overall consistency in the illumination in the dehazed results. Additionally, we propose an improved multi-scale feature extraction structure that allows the network to reconstruct the texture of the dense haze regions by leveraging long-range features.