# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Results for Multi-Type Degradation Image Enhancement  
After applying the student model after knowledge distillation to enhance the three different types of degraded images, namely, rainy, snowy, and hazy images, we conducted a thorough visual performance evaluation to assess the qualitative improvement achieved. Figure 11 illustrates the restored results for three types of images, including rainy images (top left), snowy images (bottom left), and hazy images (right). The four teacher models were first trained on four datasets separately. Then, the student model was retrained on the mixed dataset (detailed in Section 5.4).  
For the rainy images, our method effectively reduced the noise and enhanced image clarity. The restored images exhibit a noticeable reduction in rain streaks and distortion,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Encoder/Decoder Number** & **Params (M)** & **PSNR** & **SSIM** & **UIQM** & **UICM** & **UISM** & **UIConM** \\ \hline
2 & 1.99 & 22.3418 & 0.8942 & 3.064 \(\pm\) 0.25 & 7.304 \(\pm\) 3.154 & 6.86 \(\pm\) 0.649 & 0.233 \(\pm\) 0.058 \\
3 & 8.35 & 23.2525 & 0.9048 & 3.046 \(\pm\) 0.295 & 7.182 \(\pm\) 3.129 & 6.791 \(\pm\) 0.764 & 0.234 \(\pm\) 0.06 \\
4 & 31.35 & **26.4521** & **0.9286** & **3.141 \(\pm\) 0.274** & 5.436 \(\pm\) 3.211 & **6.851 \(\pm\) 0.598** & **0.270 \(\pm\) 0.051** \\
5 & 131.07 & 23.6035 & 0.9137 & 3.052 \(\pm\) 0.275 & **7.416 \(\pm\) 3.04** & 6.747 \(\pm\) 0.821 & 0.238 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of different numbers of layers on the UIEB dataset. Bold text denotes the highest value for each measurement.  
Figure 11: Visual comparison of the original and enhanced Images for rainy, snowy, and hazy conditions. The top column shows the original degraded images, and the last column displays the corresponding reference images. Notice the significant improvements in clarity, detail preservation, and contrast enhancement across different environmental conditions.  
Figure 10: Visual comparison of ablation experiments. The figure on the left shows the results obtained from four different model components. The figure on the right illustrates the outcomes when using DeMBFF-Net with varying numbers of encoder/decoder layers. The red frames in the images highlight areas that become clearer with underwater image enhancement.  
resulting in clearer and more defined objects and scenes. Fine details, such as textures and edges, were better preserved, leading to visually pleasing enhancements.  
Similarly, for the snowy images, our method successfully mitigated the adverse effects of snow accumulation. The enhanced images exhibit smoother surfaces, with reduced snow artifacts and improved visibility of the underlying details. The snow-covered regions were effectively cleared, revealing clearer and sharper image content.  
Furthermore, for the hazy images, our method significantly improved visibility and contrast. The restored images displayed enhanced clarity and color vibrancy, with reduced haze and improved overall visibility of distant objects. The fine details obscured by haze were recovered, resulting in crisper and more visually appealing images.  
Overall, the visual performance evaluation demonstrates the effectiveness and generalization of our proposed process in enhancing various types of degraded images. The qualitative improvements observed in the restored images highlight the capability of our method to restore image clarity, reduce artifacts, and enhance overall visual quality across different environmental conditions.