## 3 Experiments and Analysis
### Ablation Study  
To comprehensively validate the roles of each module proposed in this paper within the framework, namely the Channel Distillation (CD) module for feature extraction from the teacher's network middle layer, the UIE loss for perception-based quality recovery of underwater images, and the Assistance Teacher Generator \(G_{T}^{A}\) for achieving fusion-level performance, ablation study were conducted. Multiple models were trained on the EUVP dataset to facilitate detailed analysis and validation, and the results are presented in Table 4. In index-A, where all three modules were removed, and the proposed UIE loss was replaced  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **\# Model Param (M)\(\downarrow\)** & **GFLOPs (G)\(\downarrow\)** & **Times (s)\(\downarrow\)** & **FPS (f/s)\(\uparrow\)** \\ \hline UDCP & - & - & 1.693 & 0.591 \\ Fusion-based & - & - & 0.442 & 2.262 \\ UGAN & 38.7 & 18.14 & 0.104 & 9.615 \\ Water-Net & 1.1 & 142.9 & 0.161 & 6.211 \\ CWR & 6.1 & 42.37 & 0.136 & 7.353 \\ MSBDN-DFF & 31.4 & 16.14 & 0.143 & 6.993 \\ FFA-Net & 4.7 & 302.7 & 0.209 & 4.785 \\ Ours & 0.137 & 0.82 & 0.069 & 14.493 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the model parameter, GFLOPs, runtime and FPS for different methods. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. The red value represents the best, while the blue value represents the second best.  
by the image translation loss in pix2 pix, the performance was unsatisfactory, with only a PSNR score of 24.40 and an SSIM score of 0.77. In index-B, by incorporating the CD module for knowledge distillation learning, the scores for \(G_{5}\)-enhanced images improved to 24.56 and 0.78, respectively. In index-C, substituting the objective loss function of the teacher network with the UIE loss resulted in a significant boost in PSNR scores by 3.45 and SSIM scores by 0.04. Lastly, in index-3, with the addition of \(G_{T}^{A}\) for multi-teacher knowledge distillation, there was further improvement, with an increase of 0.19 in PSNR scores and 0.02 in SSIM scores. These ablation study effectively demonstrate the efficacy of the individual modules proposed in this paper.  
Figure 5 presents a partial example of the ablation study, illustrating the functions of each module. The channel distillation module plays a vital role in restoring detailed image textures and reducing blurring. The UIE loss contributes to color correction of the overall image and effectively mitigates the impact of scattered light. Additionally, the incorporation of \(G_{T}^{A}\) integrates diverse styles of enhanced images into the student model, enabling it to achieve a performance comparable to image fusion algorithms. This integration further enhances the quality of detailed image features. The ablation study's quantitative and qualitative results are summarized in Table 4 and Figure 5, revealing that the removal of any module diminishes the enhancement of underwater images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **CD Module** & **UIE Loss** & \(G_{T}^{A}\) & **PSNR** & **SSIM** \\ \hline A & - & - & - & 24.40 & 0.77 \\ B & ✓ & - & - & 24.56 & 0.78 \\ C & ✓ & ✓ & - & 28.01 & 0.82 \\ Ours & ✓ & ✓ & ✓ & 28.20 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study involving CD Modules, UIE loss, and \(G_{T}^{A}\) validity.  
Figure 4: Visual comparison of different types of images. The first and second rows are from the test set of EUVP, and the third, fourth, and fifth rows are from UIEDB.