# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 4 Conclusions  
In this paper, to address the challenges of color deviation and low contrast in underwater images, we present an underwater image enhancement method based on the attention mechanism and adversarial autoencoder. By incorporating positive samples from the pre-trained model and negative samples generated by the encoder, the discriminator guides the autoencoder's hidden space to approximate that of the pre-trained model. Furthermore, the encoder features, extracted using the attention mechanism, are fused with the features from the reverse medium transmittance map in the feature fusion module. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arithmetic** & **Us** & **Arithmetic** & **t/s** \\ \hline UDCP & 6.756 & FunieGAN & 15.960 \\ HE & 0.024 & ULAP & 0.823 \\ Ucolor & 3.986 & Fusion & 0.371 \\ Ours & 2.786 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average processing time per image of algorithms. Figure 11: Results of the ablation study. (**a**) Original image, (**b**) de-attentioning mechanism, (**c**) our proposed methodology. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodologies** & **SSIM** & **PENR** \\ \hline Original Image & 0.343 & 16.127 \\ De-attentioning Mechanisms & 0.910 & 23.774 \\ Ours & 0.929 & 24.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective evaluation index results of ablation study. This enhances the network's ability to respond to regions with degraded image quality. The comparison experiments demonstrate that the reconstructed images outperform the existing six algorithms. When compared with the unprocessed real underwater images, the average Natural Image Quality Evaluator value is reduced by 27.8%, the average Underwater Color Image Quality Evaluation value is improved by 28.8%, and the average Structural Similarity and Peak Signal-to-Noise Ratio values are enhanced by 35.7% and 42.8%, respectively. The method exhibits excellent visual effects, effectively restoring the real scene of underwater images and enhancing the visibility of underwater target objects. It holds the potential to be utilized by underwater robots in future applications such as exploring marine resources and cleaning the bottoms of large ships. Our proposed method is not without limitations. Primarily, our approach emphasizes underwater image color correction and contrast enhancement, but it may fall short in addressing the enhancement challenges posed by extremely turbid underwater images. To address this limitation, future research endeavors could focus on expanding the dataset to enhance the accuracy of transmission map estimation and bolster the model's overall robustness. In the future, we will continue to explore the combination of traditional methods with deep learning-based approaches to develop more innovative interaction designs. Moreover, we aim to generalize our algorithms to various other domains. Conceptualization, G.L. and G.H. ; methodology, G.L. and G.H. ; software, G.H. ; validation, G.L. ; formal analysis, Z.J. ; investigation, G.H. and C.L. ; resources, Z.J. and G.L. ; data curation, G.H. ; writing--original draft preparation, G.H. ; writing--review and editing, Z.J. and G.L. ; visualization, G.H. ; supervision, G.L. ; project administration, G.L. ; funding acquisition, Z.J. and G.L. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key Research and Development Program of China, grant number 2022 YFD2401100 and Shanghai Municipal Science and Technology Commission Innovation Action Plan, grant number 20 dz1206500. Not applicable. Our experiment was conducted on the Underwater-ImageNet Dataset [28]. I would like to express my heartful gratitude to the Shanghai Engineering Research Center of Hadal Science and Technology for their technical expertise and generous financial support. I am also deeply appreciative of the platform provided by China National Offshore Oil Corporation (CNOOC). Your contributions have been instrumental in the success of this endeavor.