# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 4 Experiments
### Experimental Evaluation on the USR-248 Dataset  
The assessment of the USR-248 dataset is presented in Table 1. The proposed DAIN is compared to some popular SR networks, namely SRCNN [29], VDSR [31], DSRCNN [56], EDSRGAN [57], SRGAN [58], ESRGAN [59], SRDRM [37], SRDRM-GAN [37], LatticeNet [60], Deep WaveNet [12], ESRGCNN [61], AMPCNet [10], and RDLN [62]. We can observe that our proposed DAIN attains comparable results when compared to popular methods. In comparison to mainstream SR methods, our method obtains up to 0.08 dB and 0.1 increases in PSNR and SSIM. Although the DAIN fails to obtain optimal values in terms of UIQM, it still achieves positive results. For example, in the case of a scale factor of \(\times 8\), compared to AMPCNet, our DAIN lags behind by 0.05 in terms of UIQM but boosts PSNR and SSIM by 0.14 dB and 0.02, respectively. Visual comparisons of the USR-248 dataset are shown in Figure 2. It is evident that our DAIN producesclearer and more natural image reconstruction. SRDRM and SRDRM-GAN generate visible reconstruction artifacts.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Scale** & **Method** &
\begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular} & **Params (M)** & **PSNR (dB)** & **SSIM** & **UIQM** \\ \hline \multirow{8}{*}{\(\times 2\)} & SRCNN [29] & 21.30 & 0.06 & 26.81 & 0.76 & 2.74 \\  & VDSR [31] & 205.28 & 0.67 & 28.98 & 0.79 & 2.57 \\  & DSRCNN [56] & 54.22 & 1.11 & 27.14 & 0.77 & 2.71 \\  & EDSRGAN [57] & 273.34 & 1.38 & 27.12 & 0.77 & 2.67 \\  & SRGAN [58] & 377.76 & 5.95 & 28.08 & 0.78 & 2.74 \\  & ESRGAN [59] & 4274.68 & 16.70 & 26.66 & 0.75 & 2.70 \\  & SRDRM [37] & 203.91 & 0.83 & 28.36 & 0.80 & **2.78** \\  & SRDRM-GAN [37] & 289.38 & 11.31 & 28.55 & 0.81 & 2.77 \\  & LatticeNet [60] & 56.84 & 0.76 & 29.47 & 0.80 & 2.65 \\  & Deep WaveNet [12] & 21.47 & 0.28 & 29.09 & 0.80 & 2.73 \\  & AMPCNet [10] & — & 1.15 & 29.54 & 0.80 & 2.77 \\  & RDLN [62] & 74.86 & 0.84 & 29.96 & 0.83 & 2.68 \\  & DAIN(ours) & 85.55 & 1.16 & **29.98** & **0.84** & 2.77 \\ \cline{2-6}  & SRCNN [29] & 21.30 & 0.06 & 23.38 & 0.67 & 2.38 \\  & VDSR [31] & 205.28 & 0.67 & 25.70 & 0.68 & 2.44 \\  & DSRCNN [56] & 15.77 & 1.11 & 23.61 & 0.67 & 2.36 \\  & EDSRGAN [57] & 206.42 & 1.97 & 21.65 & 0.65 & 2.40 \\  & SRGAN [58] & 529.86 & 5.95 & 24.76 & 0.69 & 2.42 \\  & ESRGAN [59] & 1504.09 & 16.70 & 23.79 & 0.65 & 2.38 \\ \(\times 4\) & SRDRM [37] & 291.73 & 1.90 & 24.64 & 0.68 & 2.46 \\  & SRDRM-GAN [37] & 377.20 & 12.38 & 24.62 & 0.69 & 2.48 \\  & LatticeNet [60] & 14.61 & 0.78 & 26.06 & 0.65 & 2.43 \\  & Deep WaveNet [12] & 5.59 & 0.29 & 25.20 & 0.68 & 2.54 \\  & AMPCNet [10] & — & 1.17 & 25.90 & 0.68 & 2.58 \\  & RDLN [62] & 29.56 & 0.84 & 26.16 & 0.66 & 2.38 \\  & DAIN (ours) & 21.78 & 1.18 & **26.23** & **0.70** & 2.56 \\ \hline \multirow{8}{*}{\(\times 8\)} & SRCNN [29] & 21.30 & 0.06 & 19.97 & 0.57 & 2.01 \\  & VDSR [31] & 205.28 & 0.67 & 23.58 & 0.63 & 2.17 \\  & DSRCNN [56] & 6.15 & 1.11 & 20.14 & 0.56 & 2.04 \\  & EDSRGAN [57] & 189.69 & 2.56 & 19.87 & 0.58 & 2.12 \\  & SRGAN [58] & 567.88 & 5.95 & 20.14 & 0.60 & 2.10 \\  & ESRGAN [59] & 811.44 & 16.70 & 19.75 & 0.58 & 2.05 \\  & SRDRM [37] & 313.68 & 2.97 & 21.20 & 0.60 & 2.18 \\  & SRDRM-GAN [37] & 399.15 & 13.45 & 20.25 & 0.61 & 2.17 \\  & LatticeNet [60] & 4.05 & 0.86 & 23.88 & 0.54 & 2.21 \\  & Deep WaveNet [12] & 1.62 & 0.34 & 23.25 & 0.62 & 2.21 \\  & AMPCNet [10] & — & 1.25 & 23.83 & 0.62 & **2.25** \\  & RDLN [62] & 18.23 & 0.84 & 23.91 & 0.54 & 2.18 \\  & DAIN (ours) & 5.99 & 1.26 & **23.97** & **0.64** & 2.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of different methods in the USR-248 dataset with scale factors of \(\times 2\), \(\times 4\), and \(\times 8\). Bold is the best performance.