## 3 Our Method
### Color-Correction Branch
#### 3.3.2 Architecture of Color-Correction Branch  
A color-correction branch is applied to the image-dehazing task, aiming to enhance the color information and details of the image, thereby improving the visual quality of the dehazed result. The structure of the color-correction branch is shown in Figure 6. To better  
Figure 5: Difference between RGB and Lab color space.  
handle the color information, we first convert the image from the RGB color space to the Lab color space, then integrate attention mechanisms and multilayer convolutional neural network structures into the model design for enhanced performance. Unlike previous methods that adjust color distribution based on the mean values of RGB channels [45] or compensate for weak color channels using attenuation matrices to correct color distortion [46], our approach operates in the Lab color space. Through this color-space transformation, the model can independently process lightness and color information, allowing for more precise adjustments to the color details of the image.  
We input the AB channels \(X_{AB}\) of the image. By combining the attention-map-generation module and the forward processing module, it effectively enhances the AB channels of the image. Additionally, to help the model focus more precisely on important regions of the image, we introduce an attention mechanism. This mechanism generates an attention map that highlights key regions of the image, aiding the network in focusing on critical parts of the image during the training process.  
The attention-map-generation module uses a multilayer convolutional network to extract image features and produce an attention map of the same size as the input image. This attention map is concatenated with the original AB channels and fed into the forward processing module, further enhancing the image-processing effect. Specifically, the attention-map-generation module first processes the input AB channels and produces the corresponding attention map. Then, the attention map is concatenated with the original AB channels and passed through the forward processing module for further feature extraction and enhancement.  
Additionally, in the second image-processing stage, We utilize the Convolutional Block Attention Module (CBAM) [47] to enhance details, and its specific structure is shown in Figure 7. The Convolutional Block Attention Module (CBAM) consists of two key components: the Channel Attention Module (CAM) and the Spatial Attention Module (SAM). CAM enhances feature representation by assigning different weights to channels, emphasizing the most informative ones. SAM improves spatial perception by generating a spatial attention map, assigning higher weights to key regions to enhance focus on critical spatial information. Together, these modules enable adaptive feature refinement for more effective representation learning. The key idea behind the forward processing module is to stack convolutional layers, enabling the network to learn features at different levels of the image and ultimately output the enhanced AB channels.  
Finally, the enhanced AB channels \(\hat{X}_{AB}\) are output through the final convolutional layer. By separately handling lightness and color information and introducing attention mechanisms to focus on important regions, the model provides a more refined approach  
Figure 6: Illustration of color-correction branch.  
to image dehazing. This approach not only recovers the lightness information but also reconstructs more of the fine details during the dehazing process.