## 1 Introduction
However, the computational efficiency of this method for high-resolution images needs to be improved. [PERSON] et al. [28] developed an end-to-end deep learning architecture based on U-Net, incorporating discrete wavelet-transform jump connections to preserve high-frequency features, mitigating color distortion caused by red channel absorption. [PERSON] et al. [29] in  
Figure 1: Examples of color difference in image dehazing using different models. troduced a two-stage progressive network, which adjusts the pixel values of the three color channels independently and employs a channel attention mechanism for color restoration. Deep learning-based methods have significantly advanced the field of image dehazing. These methods offer robust performance in tasks such as color restoration and image enhancement. [PERSON] et al. [30] proposed a multi-scale cascaded Transformer network with adaptive group attention to dynamically select complementary channels, addressing image color degradation caused by light absorption and scattering. Similarly, [PERSON] et al. [31] introduced a multi-domain query cascade Transformer network, which integrates local transmission and global illumination features using a multi-domain query cascade attention mechanism. However, the large number of parameters in these models often requires substantial storage space and computational resources, which limits their practicality. Therefore, it is crucial to design lightweight models for dehazing applications to address these efficiency challenges. It is difficult to strike a balance between preserving image color and ensuring high-quality dehazing effect in image-dehazing tasks. In order to improve the comprehensive performance of the dehazing algorithm, we propose a dual-branch network that focuses on image dehazing along with color distortion correction. These two branches are the image-dehazing branch and the color-correction branch. The dehazing branch is to enhance the processing capability of the image by multi-scale feature extraction and fusion, which utilizes different levels of feature representation to gradually enhance the details of the image, and also enhances the multi-scale information of the image by up- and downsampling operations. The branch uses attention mechanisms (channel attention, CA, and pixel attention, PA) to dynamically adjust the important parts of the feature map to further optimize the effect of image processing. These attention mechanisms help the network to focus on the regions that are most important to the task during processing, thus enhancing the effectiveness of the model. The color-correction branch aims to enhance the color information and details of an image. We introduce Lab color space, attention mechanism, and multilayer convolutional neural network structure into the model design. In order to better process the color information of the image, we convert the image from RGB color space to Lab color space. A multilayer convolutional network is utilized to extract image features and generate an attention map with the same size as the input image. This attention map is connected with the original AB channel and input to the forward processing module to further enhance the image processing. The features acquired from these two branches are effectively merged, resulting in high-quality, visually satisfying images. In summary, our main contributions are as follows:  
1. An effective end-to-end two-branch image-dehazing network CCD-Net is proposed. It focuses on image clarity and color features to obtain high-quality images. 2. A color-correction branch is proposed to utilize features from different color spaces to avoid the difficulty of feature extraction in ordinary RGB color space. In addition, we introduce the Convolutional Block Attention Module (CBAM) into the Color-Correction Network to improve the feature-extraction capability of the network and effectively recover the missing color and detail information. 3.