# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 3 Multi-Feature Fusion Image Dehazing Network
### Lightweight Gated Channel Attention  
The FFA-Net [27] utilizes SE-Net [28] as the channel attention mechanism to capture the interrelationship among feature channels. However, this design introduces two issues. First, SE-Net relies on two FC layers to handle channel embeddings, which imposes significant demands on computational resources. Secondly, due to the complexity of the parameters in the FC layers, understanding and analyzing the inter channel interactions among different layers becomes exceedingly challenging. FC layers implicitly learn the channel relationships, leading to neuron output behaviors that are difficult to interpret. To address these problems, we propose a lightweight, interpretable, and efficient attention mechanism LGCA. Figure 5 illustrates the detailed structure of LGCA, which comprises three components: a global context embedding layer, a channel normalization layer, and a gated adaptive layer. Initially, a parameterless \(\ell_{1}+\ell_{2}\)-norm is employed by the global context embedding layer to aggregate global context information. To adjust the proportions of \(\ell_{1}\)-norm and \(\ell_{2}\)-norm regularization and to endow the LGCA with learning capabilities, a global context embedding parameter \(\alpha\) is introduced, which is capable of embedding global context information and adjusting the weights of individual channels before channel normalization. Subsequently, the stability and generalizability of the algorithm are ensured by the channel normalization layer. Finally, based on the output of the channel normalization layer, the gated adaptive layer adjusts the input channel features using gated weights \(\gamma\) and bias \(\beta\), and competition and cooperation among features are promoted through a gated \"_tanh_\" activation function. It has been demonstrated by experiments that the image dehazing effect is enhanced by LGCA while utilizing a reduced number of parameters. Figure 4: Structure of ultmi-feature fusion module. \[\widehat{\chi}=\widehat{F}(\chi|\alpha,\gamma,\beta),\alpha,\gamma,\beta\in\mathbb{N }^{C} \tag{7}\]  
where \(\chi\in\mathbb{N}^{C*H*W}\) is a feature map, where \(H\) and \(W\) are the height and width of the feature map and \(C\) is the channel of the map. Let \(\chi=[\chi_{1},\chi_{2},...,\chi_{C}]\), and \(\chi_{c}\) is a corresponding feature for each channel in \(\chi\), where \(\chi_{c}=[X_{i}^{i,i}]_{H\times W}\in\mathbb{N}^{H\times W}\) and \(c=\{1,2,...,C\}\). The final output result \(\widehat{\chi}\) is determined collectively by the trainable parameters \(\alpha\), \(\gamma\), and \(\beta\), where \(\alpha=[\alpha_{1},\alpha_{2},...,\alpha_{C}]\), \(\gamma=[\gamma_{1},\gamma_{2},...,\gamma_{C}]\), and \(\beta=[\beta_{1},\beta_{2},...,\beta_{C}]\). It is worth noting that the parameter complexity of LGCA is \(O(C)\), significantly less than the \(O(C^{2})\) of SE-Net. Global Context Embedding Layer: The performance of an algorithm is often constrained by the quality and quantity of the dataset. The aggregation of global context information can provide a broader and more comprehensive data background, thereby improving the precision of understanding input data. Importantly, the integration of global context information can help mitigate the influence of local features on algorithm performance, thereby enhancing the model's robustness and generalizability. [PERSON] et al. pointed out that a regularization function can establish connections among neurons [38,39]. In this regard, \(\ell_{1}\)-norm is capable of reducing irrelevant feature parameters that have minimal or no impact on the task to zero, exploiting the sparsity of the network to achieve rapid feature selection. \(\ell_{2}\)-norm, conversely, assigns higher weights to critical features and lower weights to secondary features, thereby preventing model overfitting. Based on these principles, LGCA has designed a global information embedding layer, the implementation of which is detailed in Equations (8)-(10).