# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 5 Conclusions  
We proposed a novel end-to-end adaptive enhancement dehazing network, called IFE-Net, to address the challenge of single-image dehazing. IFE-Net consists of a multiscale feature extraction block, an attention mechanism (AM) module, and a bilateral constrained rectifier linear unit (BCReLU). Considering the cumulative errors that may arise from estimating atmospheric light and transmission maps separately, IFE-Net estimates a parameter that is unified by both. Its novel network design effectively performs feature extraction. In addition, we designed an attention mechanism (AM) module to address the varying importance of information in different regions. The importance of BCReLU in image restoration was also demonstrated through experiments. We compared IFE-Net with other dehazing methods using PSNR and SSIM, and the results show that IFE-Net achieved good scores for both indicators. At the same time, we used subjective criteria to analyze the results obtained via different methods on natural haz  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**SOTS**} & \multicolumn{2}{c}{**ITS**} \\ \hline Metric & PSNR & SSIM & PSNR & SSIM \\ \hline AOD & 22.12 & 0.903 & 22.39 & 0.917 \\ \hline AOD + AM & 24.13 & 0.904 & 23.99 & 0.920 \\ \hline IFE without AM & 23.16 & 0.902 & 23.77 & 0.921 \\ \hline IFE + AM & **24.63** & **0.905** & **25.62** & **0.925** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effectiveness of AM module.  
Figure 9: Comparison of the visual effects of AM block.  
is that the proposed IFE-Net combines feature extraction blocks, attention mechanism, and a BCReLU activation function, making it significantly effective in natural and synthetic image dehazing. Although our IFE-Net has a simple structure, it shows strong capabilities in haze removal. The experimental results confirm the superiority and efficiency of IFE-Net. At present, IFE-Net has achieved good results in dehazing, and another promising area for our future research is to apply it to image enhancement algorithms.  
Methodology, C.L.; Software, C.L.; Writing--original draft, C.L.; Writing--review & editing, G.L.; Supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
This researchThis research is supported by National Nature Science Foundation of China (NSFC) 11901065 and Chengdu University of Information Technology(KYTD202243).  
Not applicable.  
The source data NYU2 Depth Database are openly available at [[[https://doi.org/10.1007/978-3-642-33715-4_54](https://doi.org/10.1007/978-3-642-33715-4_54)]([https://doi.org/10.1007/978-3-642-33715-4_54](https://doi.org/10.1007/978-3-642-33715-4_54))], reference number [49]. The source data RESIDE dataset are openly available at [[[https://doi.org/10.1109/TIP.2018.2867951](https://doi.org/10.1109/TIP.2018.2867951)]([https://doi.org/10.1109/TIP.2018.2867951](https://doi.org/10.1109/TIP.2018.2867951))], reference number [55]. All the extension data are generated by ourselves and freely available to other researchers.  
We thank the anonymous reviewers for their valuable comments. The authors declare no conflict of interest.