# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 4 Experiments
### Visual Comparisons  
We randomly selected images from the real-world underwater datasets UIEB, C60, and U45, and present them in Figure 5. The images recovered by the PGN have a clearer texture and look more natural. In the sample from the UIEB dataset, our method recovers both background and foreground well when the illumination is complex. For the position with light spots, the image enhanced by PGN has neither overbrightness nor artifacts. The most traditional methods only enhance the contrast of the image, but fail to recover the color distortion. Traditional physical-based methods depend on limited parameters of simplified imaging models and cannot recover the degradation completely. Although the results of deep learning-based methods can sometimes remove color distortion well, these restored images have low contrast, which leads to the entire image appearing relatively dark, with colors that are not realistic or vivid. Although the image enhanced by MLLE [6] appears relatively bright, the colors exhibit noticeable distortion and lack the necessary sense of realism. The task of enhancing the images in C60 is very challenging. Traditional methods including IBLA, RGHS, and ULAP are almost completely ineffective in this difficult situation. In the sample from the C60 dataset, the result of FUnIE_GAN shows obvious distortion, with poor visibility of the shapes of the fish and severe blurring of the edges. The other deep learning-based methods improve the color distortion problem, but overall, the visual effects of the results are not satisfactory enough. In the second sample, our method can effectively remove the color distortion and obtain more natural enhanced foreground and background results. The results from almost all methods except MLLE and PGN have noticeable blue or green color distortion. The MLLE [6] model can eliminate the obvious green color distortion, but it also generates more artificial details. Only our PGN effectively removes color distortion while restoring a more natural enhanced image. For the first samples from U45, our PGN and TACL obtain the best enhanced results. Other methods have the problem that color distortion is not completely removed or the enhanced images are not natural enough. In the second sample from U45, results from almost all methods except MLLE and PGN have noticeable color distortion. Although MLLE is better at removing color distortion, its results have less color richness. PGN can recover the original rich color of the shells well, although the color distortion remains in the upper left corner of the image. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **IBLA** & **RGHS** & **ULAP** & **FGAN** & **Water-Net** & **Ucolor** & **MILE** & **TACL** & **PGN** \\  & **[19]** & **[5]** & **[20]** & **[10]** & **[15]** & **[8]** & **[6]** & **[33]** & \\ \hline \multirow{4}{*}{UIEB} & PSNR \(\uparrow\) & 15.31 & 19.72 & 16.33 & 19.82 & 23.82 & 22.28 & 18.22 & 23.22 & **24.73** \\  & SSIM \(\uparrow\) & 0.647 & 0.839 & 0.758 & 0.835 & 0.889 & 0.904 & 0.727 & 0.854 & **0.922** \\  & UISM \(\uparrow\) & 6.565 & 7.048 & 6.943 & 7.133 & 7.181 & 7.191 & **7.468** & 7.203 & 7.215 \\  & MUSIQ \(\uparrow\) & 45.40 & 46.14 & 45.21 & 42.57 & 44.22 & 46.61 & **52.1** & 45.08 & 47.13 \\  & COSINE \(\uparrow\) & 0.741 & 0.853 & 0.789 & 0.865 & 0.912 & 0.927 & 0.791 & 0.886 & **0.952** \\ \hline \multirow{2}{*}{C60} & UISM \(\uparrow\) & 2.16 & 2.56 & 6.71 & 7.14 & 2.61 & 2.48 & 3.12 & 2.95 & **7.40** \\  & MUSIQ \(\uparrow\) & 40.31 & 39.65 & 43.18 & 40.37 & 40.23 & 40.07 & 40.34 & 38.63 & **46.61** \\ \hline \multirow{2}{*}{U45} & UISM \(\uparrow\) & 7.04 & 7.36 & 6.98 & 7.14 & 7.19 & 7.24 & 7.47 & 7.24 & **7.54** \\  & MUSIQ \(\uparrow\) & 45.58 & 46.14 & 45.88 & 43.45 & 45.97 & 47.28 & **51.67** & 43.89 & 48.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Image Quantitative evaluation of different methods on the UIEB [15], C60 [15], and U45 [32] datasets. The best results are highlighted in bold, and the second-best results are underlined.