## 2 Related Work
### Learning-Based Methods  
Deep learning applications in image dehazing are notable for their superior capabilities and flexibility. With the power of convolutional neural networks, these algorithms can automatically capture features highly correlated with haze and accurately predict scene transmittance or directly generate clear images without fog. Therefore, deep learning technology has taken a central position in the research of image dehazing, which not only revolutionizes the image-processing and computer-vision fields, but also injects new development potential into these fields.  
[PERSON] et al. [16] proposed an end-to-end image-dehazing network named DehazeNet, using convolutional neural networks to address the shortcomings of traditional image-dehazing methods, which utilizes deep learning to compute the transmission rate and atmospheric coefficients to obtain the dehazed image. Similarly, [PERSON] et al. [17] proposed multi-scale convolutional neural networks (MSCNNs) and [PERSON] et al. [36] proposed a joint dehazing network for transmission maps; [PERSON] et al. [37] creatively proposed a normalized dehazing network in response to the fact that the previous networks mostly estimated the atmospheric light value and transmittance separately, namely the All-in-one Dehazing Network (AOD-Net), which unifies the two unknown parameters into one parameter and reduces the computation and error.  
The end-to-end algorithm does not require consideration of the foggy-image-degradation causes or the imaging process. The network takes foggy images as input and, after learning, outputs a clear image. [PERSON] et al. [38] proposed an enhanced pix2 pix dehazing network, which treats image dehazing as an image-to-image conversion problem. [PERSON] et al. [39] introduced an end-to-end gated context-aggregation network for image dehazing. [PERSON] et al. [40] proposed a feature-fusion attention (FFA) network, which processes different types of feature information in order to realize image dehazing. [PERSON] et al. [41] proposed a compact image-dehazing method based on contrast learning, which further constrains the dehazing problem by introducing negative samples and fully exploiting the information in the negative samples to upper and lower bounds of the solution space. The above end-to-end dehazing network that does not rely on atmospheric degradation models can remove fog from hazy images to a certain extent, but it over-extracts global multiscale features at the input resolution in the dehazing process, and lacks the ability to focus on local detailed texture information (Table 1).  
Dehazed images often suffer from color distortion due to a focus on contrast enhancement over color accuracy. To address this issue, we introduce a novel dual-branch framework that integrates dehazing and color correction into a unified model, ensuring effective haze removal while enhancing chromatic fidelity.  
This section first provides an overview of the proposed CCD-Net's overall structure, offering a comprehensive understanding of the network architecture. It then proceeds to introduce the individual components, the dehazing branch and color-correction branch in detail, analyzing their respective functions and design objectives. Finally, the section discusses the design of the loss function and its role in the training process.