# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 3 Experimentation and Analysis
### Ablation Studies  
To assess the effectiveness of the proposed UW-AAE network and its key module, the attention mechanism, ablation experiments were conducted. Figure 11 illustrates the comparison between the results obtained with the canny operator [32] processing the original image, without the inclusion of the attention module, and the results obtained using this paper's algorithm after enhancement. It is evident that the regions with significant visual quality improvement are marked in red on the matrix.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image** & **Input** & **UDCP** & **HE** & **Ucolor** & **Fusion** & **FunieGAN** & **ULAP** & **Ours** \\ \hline image1 & 16.127 & 13.932 & 24.210 & 23.789 & 24.330 & 16.764 & 17.941 & **24.979** \\ image2 & 16.688 & 10.048 & 21.723 & 23.549 & **36.612** & 16.083 & 17.653 & 26.808 \\ image3 & 17.381 & 11.494 & 28.295 & 21.171 & 21.582 & 18.506 & 15.907 & **29.935** \\ image4 & 18.383 & 16.138 & 15.345 & 21.065 & 21.843 & 24.191 & 24.051 & **27.226** \\ image5 & 18.125 & 19.828 & 23.999 & 23.334 & 25.080 & 26.781 & 23.672 & **28.045** \\ image6 & 19.768 & 11.895 & 20.546 & 20.126 & 19.965 & 20.795 & 19.828 & **27.230** \\ image7 & 8.958 & 7.803 & 13.534 & 9.399 & 10.586 & 10.646 & 9.184 & **11.648** \\ Image8 & 27.830 & 26.484 & 14.875 & 19.192 & 23.601 & 22.528 & 14.692 & **28.765** \\ average & 17.908 & 14.703 & 20.316 & 20.203 & 22.950 & 19.537 & 17.866 & **25.580** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of PSNR evaluation index results on test set B. The values in bold indicate the maximum PSNR score in each group. A higher PSNR score indicates less distortion in the enhanced image compared to the reference image.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Input** & **UDCP** & **HE** & **Ucolor** & **Fusion** & **FunieGAN** & **ULAP** & **Ours** \\ \hline image1 & 0.343 & 0.405 & 0.918 & 0.734 & 0.801 & 0.384 & 0.442 & **0.929** \\ image2 & 0.639 & 0.445 & 0.810 & 0.839 & **0.983** & 0.642 & 0.757 & 0.806 \\ image3 & 0.716 & 0.602 & 0.814 & 0.838 & 0.780 & 0.778 & 0.679 & **0.937** \\ image4 & 0.773 & 0.651 & 0.618 & 0.741 & 0.778 & 0.824 & 0.741 & **0.860** \\ image5 & 0.751 & 0.709 & 0.857 & 0.859 & 0.840 & 0.791 & 0.796 & **0.872** \\ image6 & 0.645 & 0.589 & 0.713 & 0.748 & 0.739 & 0.712 & 0.712 & **0.938** \\ image7 & 0.176 & 0.045 & 0.349 & 0.184 & 0.256 & 0.153 & 0.069 & **0.364** \\ Image8 & 0.811 & 0.721 & 0.599 & 0.778 & 0.648 & 0.693 & 0.689 & **0.883** \\ average & 0.607 & 0.521 & 0.710 & 0.715 & 0.728 & 0.622 & 0.611 & **0.824** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of SSIM evaluation index results on test set B. Bold indicates the maximum value of each group of SSIM, where a higher SSIM value indicates that the image is more similar to the true reference image.  
As depicted in Figure 11, when compared with the original unprocessed image, the colors of the image have been noticeably corrected, leading to an improvement in overall image clarity. Additionally, the results obtained through the proposed method exhibit enhanced edge detection capabilities, indicating that the image retains more content information and possesses a superior ability to enhance image details.  
SSIM and PSNR were selected as the objective evaluation indices for the ablation experiments, and the corresponding results are presented in Table 5. It is evident from the table that the algorithm proposed by us, equipped with the attention mechanism module, achieves the most favorable results among all the variations.