## 4 Experiments and Analysis
### Visual Comparisons  
In the case of the T90 dataset, a subset of images from the UIEB dataset was carefully chosen for conducting a visual comparison against seven state-of-the-art methods. These images were meticulously classified into various categories, encompassing yellow-toned, green-toned, blue-toned, low light, shallow-water areas, and deep-water areas. Figures 3 and 4 showcase the performance of each method. Shallownet succeeded in enhancing contrast, but fell short in improving the quality of yellow-toned and green-toned underwater images. UWGAN exhibited notable improvements in yellow-toned images; however, oversaturation issues arose in many instances. PRW-Net's applicability was limited across a wide range of scenarios, as it introduced additional chromatic aberrations or artifacts in deep-water areas and low-light conditions. UIC\({}^{\star}\)2-Net employed a multicolor spatial encoder for natural color processing, but inaccurate transmission map estimation resulted in contrast and saturation degradation. PUIE-Net and NU2 Net encountered challenges with overall darkness and low contrast in a majority of the processed images. In contrast, our method effectively combines the strengths of multiple features, resulting in visually pleasing outcomes in terms of contrast, saturation, and detail processing. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Efficiency**} \\ \cline{2-5}
**Method** & **GFLOPs** & **\# of Params (M)\(\downarrow\)** & **Runtime (s)\(\downarrow\)** & **FPS (f/s)\(\uparrow\)** \\ \hline WaterNet(TIP’19) [5] & 193.70 & 24.81 & 0.680 & - \\ FUNIE GAN(RAL’20) [43] & - & 7.02 & - & - \\ PRW-Net(ICCVW’21) [15] & 223.40 & 6.30 & 0.216 & 4.624 \\ Shallow-uwnet(AAAI’21) [38] & 304.75 & **0.22** & **0.031** & **31.836** \\ Ucolor(TIP’21) [14] & 443.85 & 157.42 & 2.758 & - \\ UIEC\({}^{\star}\)2-Net(SPIC’21) [50] & 367.53 & 0.53 & 0.174 & 5.742 \\ PUIE-Net(ECCV’22) [16] & 423.05 & 1.40 & 0.071 & 14.194 \\ UID-SPNet(ACCV’22) [18] & 15.24 & 37.31 & 0.059 & 16.769 \\ NU2 Net(AAAI’23,Oral) [17] & 146.64 & 3.15 & 0.024 & 42.345 \\ MFEF(EAAAI’23) [19] & - & 2.79 & - & - \\ \hline Ours & **90.02** & 0.08 & 0.070 & 14.552 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The efficiency evaluation was conducted using \(1280\times 720\) images as the input on an RTX 3090 graphics card. The results are presented below, with the best-performing outcomes indicated with underlining and the second-best results highlighted in **bold**. The symbol \(\uparrow\) denotes that higher values indicate better performance, while \(\downarrow\) signifies that lower values are desirable. This evaluation aimed to assess the computational efficiency of the methods under consideration and provide insights into their relative performance based on the given criteria. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**EUVP-Dark (1000) [43]**} \\ \cline{2-5}
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **UCIQE\(\uparrow\)** & **UIQM\(\uparrow\)** \\ \hline Shallownet(AAAI’21) [38] & 19.6058 & 0.8167 & **0.5537** & 2.9411 \\ NU2 Net(AAAI’23,Oral) [17] & **20.0908** & **0.8631** & 0.5536 & 3.0775 \\ \hline Ours & 20.4129 & 0.8802 & 0.5610 & **3.0466** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on the EUVP-Dark [43] dataset. Best results are underlined. The second-best results are in **bold**. \(\uparrow\) represents that higher is better. Additionally, Figures 5-7 verify the generalization performance of LPS-Net, which can be finely enhanced for underwater degraded images of different qualities. Our method exhibited quite compatible color and detail recovery, enhancing the entire degraded image and making its contrast and texture details meet the sensory requirements of the human eyes. That is beneficial from our well-designed DAEB, MLB, and GFU. Figure 3: Visual comparison of enhancement results on the T90 testing set. Further, we also give an intuitive comparison with two previous SOTA methods on the EUVP dataset.