# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 2 Related Work  
In recent years, we have witnessed significant advancements in single-image dehazing. The existing methods can be broadly categorized into two types: physics-based methods and deep-learning-based methods. Physics-based methods rely on atmospheric scattering models [1] and handcrafted priors, focusing on filtering haze-free images using statistical analyses or observations. Examples include the Dark Channel Prior (DCP) [2], Color Lines Prior [3], Color Attenuation Prior [4], Sparse Gradient Prior [5], Maximum Reflectance Prior [6], and Non-Local Prior [7]. For example, the DCP [2] identifies the dark channel prior to modeling the characteristics of haze-free images. This is based on the assumption that in natural scenes without haze, the local minimum intensity values across the RGB channels will be nearly zero. However, handcrafted priors mainly come from empirical observations and cannot accurately characterize the haze formation process. In contrast to physics-based methods, deep-learning-based methods employ convolutional neural networks (CNNs) to learn image priors [8; 9; 10; 11; 12; 13] from hazy to clear images [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. For example, [PERSON] et al. [8] generate restored images by reformulating an atmospheric scattering model. [PERSON] et al. [14] introduce Transformers into image dehazing, combining the global modeling capability of Transformers with the local representation power of CNNs. FSDGN [18] reveals the relationship between haze degradation and frequency characteristics, jointly exploring information from the frequency and spatial domains for image dehazing. [PERSON] et al. [28] proposed a paradigm for real image dehazing from the perspective of synthesizing more realistic hazy data and introducing more robust priors into the network. [PERSON] et al. [27] proposed a curriculum contrastive regularization approach, leveraging the dehazing results from other existing methods to provide better lower bound constraints. Despite the remarkable progress achieved by these methods, they still suffer from content and color information loss in scenarios with dense haze. The main reason for this is that hazy images contain limited original information under challenging conditions, hindering these methods' ability to map the information effectively. Recently, DDPMs (Denoising Diffusion Probabilistic Models) [29; 30] have garnered significant attention due to their powerful generative capabilities. A DDPM can generate high-quality images both unconditionally [31; 32; 33] and conditionally [34; 35; 36], demonstrating exceptional performance in the field of image synthesis. Leveraging its unique advantages, the diffusion model has been widely applied to various visual tasks, including image dehazing, super-resolution restoration, and image generation. Through its step-by-step denoising process, it can produce highly detailed and realistic images, bringing new breakthroughs to the field of computer vision. However, the DDPM does not consider the physical characteristics of the dehazing task, which limits its ability to complete information. For instance, the significant differences in the distribution between images with dense haze and clear images make it challenging for the DDPM to adapt to images with varying levels of haze density. Additionally, although the encoder-decoder-based denoising network architecture has demonstrated strong capabilities in diffusion models, the inherent single-scale feature extraction and limited spatial information fusion methods still lead to issues such as the insufficient utilization of the long-range features and inconsistent overall illumination in the generated results. Specifically, the traditional methods use sliding windows for global feature extraction and rely on skip connections for hierarchical feature concatenation.