## 3 Experiments and Analysis
### Objective Evaluation  
Table 1 presents the average MSE, PSNR, and SSIM values of the enhancement results on the EUVP and UIEBD datasets, showcasing the competitiveness of our method. Our approach, MTUW-GAN, achieves a remarkable performance in underwater image enhance  
ment, as evidenced by achieving the highest scores in MSE, PSNR, and SSIM on the EUVP test set. This outcome underscores the network architecture's capability to restore colors and revive image details in underwater images. Compared to the second-best method, our approach exhibits improvements of 31.40 in the MSE, 1.04 in the PSNR, and 0.06 in the SSIM. Additionally, on the UIEBD test set, our method attains the best PSNR and SSIM scores, and the second-best MSE score. These findings demonstrate that our method excels in handling image details and local contrast in degraded images, while also displaying robust generalization capabilities across a wide range of underwater scene images. In contrast to prevalent methods based on image fusion and multi-scale feature fusion, our proposed approach exhibits a superior performance. Table 2 presents the average UICM, UISM, UIConM, and UIQM results for the enhanced images. Notably, our proposed method consistently achieves the highest UIQM score across the board. Among the three component metrics, UISM and UIConM obtain the highest scores, indicating superior performance in terms of image sharpness and contrast enhancement. Although the UICM score ranks third among the compared methods, it is worth noting that the UICM, being a color metric component, tends to favor images with a higher color saturation. Consequently, certain underwater images may receive inflated color metric scores when processed by fusion-based methods, leading to discrepancies between score judgments and the visual perception of human observers. Nonetheless, our algorithm remains highly competitive in enhancing the clarity and contrast of underwater images, delivering an exceptional performance akin to image-fusion-based approaches. In addition to evaluating performance, our focus also extends to comparing the efficiency of each algorithm. Table 3 presents these data, indicating that our model exhibits a superior efficiency across multiple metrics. Specifically, MTUW-GAN demonstrates remarkable advantages in terms of the number of parameters, GFLOPs, and the running speed. MTUW-GAN requires only 0.137 M parameters, which is more than 87% less than \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**EUVP**} & \multicolumn{2}{c}{**UIEBD**} \\ \cline{2-6}  & **MSE\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MSE\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** \\ \hline UDCP & 2319.73 & 15.90 & 0.55 & 4341.56 & 12.45 & 0.52 \\ Fusion-based & 1068.93 & 18.58 & 0.68 & 583.66 & 21.63 & 0.75 \\ UGAN & 817.39 & 20.29 & 0.71 & 970.40 & 21.36 & 0.73 \\ Water-Net & 385.44 & 23.89 & 0.78 & 594.83 & 22.14 & 0.75 \\ CWR & 263.76 & 24.99 & 0.76 & 782.46 & 20.71 & 0.72 \\ MSBDN-DFF & 187.84 & 26.45 & 0.73 & 170.40 & 24.60 & 0.79 \\ FFA-Net & 154.96 & 27.16 & 0.75 & 240.25 & 24.51 & 0.80 \\ Ours & 123.56 & 28.20 & 0.84 & 205.71 & 25.88 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average MSE, PSNR, and SSIM values of the enhancement results on the EUVP and UIEBD datasets. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. Red represents the best, while blue represents the second best.