# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Ablation Experiments  
In order to assess the impact of the DeMBFF mechanism proposed in this paper, ablation experiments were conducted on the UIEB dataset. Four variations of the model were evaluated:  
1. w/o DeMBFF: A base U-Net model without an MBFF or denoising block, which includes a simple encoder, decoder, and jump connections;
2. With a DB: A base U-Net model with a denoising block (DB) in the encoder and decoder;
3. With MBFF: A base U-Net model with MBFF and without a DB in the encoder and decoder;
4. With DeMBFF: The full model that includes encoders and decoders with the DeMBFF mechanism.  
The results of the ablation experiments are presented in Table 5. The addition of a DB and MBFF to the base model improves the PSNR, SSIM, and UIQM. The combination of DeMBFF achieves a significant improvement compared to the other three models, highlighting the effectiveness of DeMBFF in enhancing the quality of underwater image restoration.  
As shown in Figure 10, the results confirm that the DdMBFF mechanism can significantly reduce color bias and blurring in underwater images. It is obvious that the original underwater image in the first column contains multiple degradation phenomena: both color bias and blurring problems. The base network model with DB, shown in the third column, can somewhat reduce the blurring problem. However, some areas still lack detailed restoration (as indicated by the red box). In contrast, the model with MBFF (the fourth column) promotes the details when compared to the model with DB. The full model (with DeMBFF) has a balance between color bias and sharpness in the underwater images and effectively restores the details. The results validate that our approach maintains the most valuable information for enhancement through feature fusion.  
In order to assess the impact of the number of encoder/decoder layers on the performance of DeMBFF-Net, we varied the number of layers from 2 to 5. As the number of layers increases, both the computational load for feature fusion and the number of parameters increase. The results of this investigation are presented in Table 6. Specifically, the parameter count rose from 31.35 M to 131.07 M when the number of layers increased from 4 to 5. This significant increase indicates a considerable rise in computational overhead with additional layers. At the same time, the performance dropped as the layer increased, possibly due to overfitting on the training dataset. In order to find an optimal balance between performance and efficiency, we chose to set the number of encoder/decoder layers to 4 for our experiments. This configuration offers a trade-off that ensures reasonable computational demands while maintaining acceptable performance levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Ablation** & **Denoising Block** & **MBFF** & **PSNR** & **SSIM** & **UIQM** \\ \hline \multirow{3}{*}{UIEB} & w/o DeMBFF & \(\times\) & \(\times\) & 22.7648 & 0.8706 & 3.031 \(\pm\) 0.311 \\  & with DB & \(\checkmark\) & \(\times\) & 24.5812 & 0.8535 & 3.042 \(\pm\) 0.302 \\  & with MBFF & \(\times\) & \(\checkmark\) & 24.9138 & 0.8598 & 3.078 \(\pm\) 0.323 \\  & with DeMBFF & \(\checkmark\) & \(\checkmark\) & **26.4521** & **0.9286** & **3.141 \(\pm\) 0.274** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation experiments on the UIEB dataset. The \(\times\) and \(\checkmark\) notations indicate the presence and absence of each component, respectively. Bold text denotes the highest value for each measurement.