# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 4 Experiments
### Experimental Evaluation on the UFO-120 Dataset  
In the case of Table 2, our proposed DAIN demonstrates competitive performance, particularly achieving the highest values in terms of PSNR. Our DAIN is slightly inferior to Deep WaveNet in terms of SSIM and UIQM, but the gap is at most 0.05. The quantitative results of AMPCNet lag far behind our method. Compared with transformer-based methods such as URCT and RDLN, our method yields a 0.42 dB improvement in PSNR at the scale factor of \(\times\)2. In the case of \(\times\)4, our DAIN has better performance, obtaining a 0.06 increase in SSIM compared to URCT. Additionally, the DAIN does not surpass the RDLN in terms of UIQM, but the margin is 0.02 at a scale factor of \(\times\)4.  
The visual comparisons of the UFO-120 dataset are presented in Figure 3. Our proposed DAIN consistently delivers superior results and effectively recovers finer texture features, bringing it closest to the HR image. Notably, LatticeNet exhibits over-saturation, while AMPCNET and Deep WaveNet result in significant blurring artifacts and distortions. In contrast, our proposed method is better equipped to mitigate these adverse effects.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Params (M)**} & \multicolumn{3}{c}{**PSNR (dB)**} & \multicolumn{3}{c}{**SSIM**} & \multicolumn{3}{c}{**UIQM**} \\ \cline{2-13}  & \(\times\)**2** & \(\times\)**3** & \(\times\)**4** & \(\times\)**2** & \(\times\)**3** & \(\times\)**4** & \(\times\)**2** & \(\times\)**3** & \(\times\)**4** & \(\times\)**2** & \(\times\)**3** & \(\times\)**4** \\ \hline SRCNN [29] & 0.06 & 0.06 & 0.06 & 24.75 & 22.22 & 19.05 & 0.72 & 0.65 & 0.56 & 2.39 & 2.24 & 2.02 \\ SRGAN [58] & 5.95 & 5.95 & 5.95 & 26.11 & 23.87 & 21.08 & 0.75 & 0.70 & 0.58 & 2.44 & 2.39 & 2.56 \\ SRDRM [37] & 0.83 & - & 1.90 & 24.62 & - & 23.15 & 0.72 & - & 0.67 & 2.59 & - & 2.57 \\ SRDRM-GAN [37] & 11.31 & - & 12.38 & 24.61 & - & 23.26 & 0.72 & - & 0.67 & 2.59 & - & 2.55 \\ Deep WaveNet [12] & 0.28 & 0.28 & 0.29 & 25.71 & 25.23 & 25.08 & 0.77 & **0.76** & **0.74** & **2.99** & 2.96 & **2.97** \\ AMPCNet [10] & 1.15 & 1.16 & 1.17 & 25.24 & 25.73 & 24.70 & 0.71 & 0.70 & 0.70 & 2.93 & 2.85 & 2.88 \\ ESRGCNN [59] & 1.53 & 1.53 & 1.53 & 25.82 & 26.19 & 25.20 & 0.72 & 0.71 & 0.70 & 2.98 & 2.96 & 2.85 \\ LatticeNet [60] & 0.76 & 0.77 & 0.78 & 25.86 & 26.13 & 25.10 & 0.71 & 0.71 & 0.70 & 2.97 & 2.94 & 2.94 \\ HNCT [63] & 0.36 & 0.36 & 0.36 & 25.73 & 25.86 & 24.91 & 0.71 & 0.71 & 0.69 & 2.96 & 2.88 & 2.84 \\ URCT [46] & 11.37 & - & 16.07 & 25.96 & - & 23.59 & **0.80** & - & 0.66 & - & - & - \\ RDLN [62] & 0.84 & 0.84 & 0.84 & 25.96 & 26.55 & 25.37 & 0.76 & 0.74 & 0.73 & 2.98 & **2.98** & 2.94 \\ DAIN(ours) & **1.16** & **1.17** & **1.18** & **26.38** & **26.62** & **25.56** & 0.76 & **0.76** & 0.72 & 2.97 & 2.94 & 2.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of different methods in the UFO-120 dataset with scale factors of \(\times\)2, \(\times\)3, and \(\times\)4. Bold is the best performance.  
Figure 2: Visual comparisons between our DAIN and popular methods were conducted utilizing the USR-248 dataset for scale factors of \(\times\)2, \(\times\)4, and \(\times\)8. Left to right: the original HR image, SRCNN, SRGAN, ESRGAN, LatticeNet, SRDRM, SRDRM-GAN, and the proposed DAIN.  
This is attributed to the fact that the DAIN comprises a series of chained DAEMs, which effectively capture both local and global image information, thereby enhancing the quality of image reconstruction.