## 3 Methods
### Structure of the Specific Network
#### 3.2.1 Structure of the Generators
Unlike traditional channel attention, which aggregates spatial information globally and may overlook location-specific features, attention is decomposed by CA into two complementary 1D encodings along the horizontal and vertical directions. This approach captures long-range dependencies while preserving precise positional information. By embedding location awareness into channel attention, CA allows the network to adaptively emphasize features that are not only important across channels but also relevant to specific spatial coordinates. This enables the generator to identify and enhance important regions, such as object boundaries, edges, and textured areas, which are often degraded or obscured in hazy images. This results in better structural preservation, reduced artifacts, and improved clarity in localized areas in practice. Thus, the integration of CA within the FBCA module helps the network focus on both channel importance and spatial position, leading to a more refined feature representation that contributes to more effective and visually coherent dehazing outcomes. Image hazing is performed by the \(\mathrm{G_{H}}\) based on the input clear image. The model structure is shown in Figure 6. The pseudocode is shown in Algorithm 3. Firstly, the transmittance estimation module is used to estimate the depth information, \(\hat{d}\), of the input clear image, and a scattering coefficient, \(\beta\), is randomly sampled within the range of [0.6, 1.8]. Then, the atmospheric scattering model is used to calculate a rough pseudo-hazy image. Finally, the rough pseudo-hazy image is refined using the U-Net network to avoid the visual unreality of the image caused by parameter estimation errors, as follows:  
\[\hat{H}=\mathrm{G_{H}}(Ce^{-\beta\hat{d}}+A(1-e^{-\beta\hat{d}})) \tag{5}\]  
Figure 4: The multi-layer feature extraction block can extract features using multiple MBConv structures and output multi-layer features. ```
Input: Input_Dehazy.//clear images. Output: Generated_Hazy. 17: Initialize:
18: Build the multi-layer feature extraction block(MFEB)
19: Build the feature fusion attention block(FBCA)
20: Build the output layers(output_Conv)
21: Build the U-Net network(UNet)
22: Method forward(Input_Dehazy)
23: features = MFEB(Input_Dehazy)
24: d = output_Conv(FBCA(Conv(features)))
25: \(\beta\) = Ramdom [0.8, 1.6]
26: Compute Generated_Hazy according to Eqs. (5)
27: Generated_Hazy = UNet(Generated_Hazy)//Refined by U-Net
```  
**Algorithm 3** Process flow of the generator, \(\mathrm{G_{H}}\)  
Figure 5: The feature fusion attention block can accept the multi-layer features extracted by the MFEB and feed them into different feature fusion blocks for fusion. The feature fusion block is described in the second box, and the residual block is described in the third box. Figure 6: The generator, \(\mathrm{G_{H}}\), can estimate the depth information of the image through feature extraction and fusion and combine the randomly generated scattering system to obtain a rough blurred image, as calculated using Equation (5). Finally, it uses the U-Net network for fine processing to obtain the final haze image.