# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 5 Conclusions  
To address the limitations of traditional convolutional neural networks (CNNs) and attention mechanisms based on fully connected layers in feature extraction, this paper introduces a novel attention-based multi-feature fusion dehazing algorithm named MFID-Net. The core of the MFID-Net algorithm lies in proposing an innovative multi-feature fusion module (FM), specifically designed to handle areas in images with high haze concentration and significantly enhance the restoration capability for large areas of similar color blocks. The FM module integrates a lightweight gated channel attention mechanism (LGCA) and a gated pixel attention mechanism (GPA), enhancing the overall performance of the algorithm. LGCA replaces the traditional fully connected layers by combining L1- and L2-norms, not only effectively capturing global contextual information but also substantially reducing the demand for computational resources. Meanwhile, GPA optimizes the flow of feature information, reinforcing crucial information while suppressing irrelevant information, further improving the dehazing effects. This innovative FM module is not only structurally concise and highly adaptable but also easily integrated into various image dehazing frameworks. Validation through comparative experiments and ablation studies shows the significant performance improvements of MFID-Net: on the RESIDE indoor dataset, the SSIM reached 37.42 and the PSNR increased to 0.9890; on the RESIDE outdoor dataset, the SSIM reached 34.21 and the PSNR improved to 0.9844. Despite the superior performance of MFID-Net in comparison to classical dehazing methods as evaluated by SSIM and PSNR, several limitations require careful consideration. Notably, the stability of the algorithm necessitates further enhancement, the training speed warrants acceleration, and the optimization of dehazing performance on real images continues to be a primary area of focus. Consequently, our forthcoming research endeavors  
Figure 13: Comparison of dehazing effects from ablation experiments on LGCT conducted on IST. (a) \(\ell_{1}+\ell_{2}\)-Norm, (b) \(\ell_{2}\)-Norm, (c) \(\ell_{1}\)-Norm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\ell_{1}\)-norm & & & \\ \(\ell_{2}\)-norm & & & \\ \(\ell_{1}+\ell_{2}\)-norm & & & \\ \hline SSIM & 36.95 & 37.11 & **37.42** \\ PSNR & 0.9862 & 0.9830 & **0.9890** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons on SOTS indoor testset for LGCT. will be dedicated to a comprehensive optimization of this model, aiming to expand the breadth of research within the domain of image dehazing tasks. Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and H.Z. ; software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]) and H.Z. ; data curation, Y.J. ; writing--original draft preparation, [PERSON] ([PERSON]), H.Z. and [PERSON]. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]) and H.Z. ; project administration, S.W. ([PERSON]) and H.Z. ; funding acquisition, [PERSON] ([PERSON]), Y.J. and H.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the Natural Science Foundation of Hunan Province (Grant No. 2021 JC0009), the Key open laboratory of high-impact weather of China Meteorological Administration, the Science and Technology on Near-Surface Detection Laboratory (Grant No. 6142414221302), the Natural Science Foundation of Jiangsu Province (Grant No. BK20220226), and the Major Program of Xiangjiang Laboratory (Grant No. 23 XJ01005). Data available on request due to restrictions (privacy and ethical). Authors [PERSON] was employed by the company Hunan Civil Aviation Administration Air Traffic Management Branch.