# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 4 Experiments
### Visual Comparisons
\(\uparrow\) demonstrates the larger the value, the better the effect. We randomly selected an image from all three subsets of the EUVP dataset, as shown in Figure 6. The degradation of images in the Underwater Dark dataset is relatively severe, in which case the existing methods cannot recover natural results, especially in the recovery of degraded colors. The images from Underwater ImageNet recovered by FUnIE_GAN [10] are too bright; this results in the restored images lacking a sense of realism. Water-net [15] and Ucolor [8] can not remove the color distortion. For the image from Underwater Scenes, the underwater light is uneven and our method can better recover the effect of the light changes, while other methods have obvious artifacts. We also show the results on the UGAN dataset in Figure 7. Compared with the state-of-the-art methods, our method not only restores the color closest to reference images but also preserves the structure and details. It can be seen that the results of IBLA and RGHS still have significant color distortion. When the scene is colorful and rich, our method can accurately recover the color features of images. Most results from the other methods are too vivid or too dim, and the image enhanced by our method is very clear for richly textured areas. Figure 5: Visual comparison on UIEB, C60, and U45 datasets. The comparison methods are arranged from left to right: Original images, IBLA [19], RGHS [5], ULAP [20], FunIEGAN [10], Water-Net [15], Ucolor [8], MLLE [6], TACL [33] and PGN. Our method performs best on color correction and is closest to the ground truth, especially when the image is rich in color. Figure 8 shows the enhancement results of PGN on real underwater images collected by ourselves. Many methods such as IBLA, RGHS, ULAP, and FUnIE_GAN cannot recover the degraded images obtained by the vision equipment of underwater vehicles. Although Water-net, Ucolor, and MLLE are able to restore color distortion to a certain extent, the results are unsatisfactory. Only TACL and PGN produced the most natural visual results. However, our PGN is capable of restoring these images with remarkable improvements in both detail and color, resulting in a highly natural effect. Figure 6: Visual comparisons on the EUVP dataset. Images from top to bottom are from Underwater Dark, Underwater ImageNet, and Underwater Scenes, respectively. The comparison methods are arranged from left to right: Original images, IBLA [19], RGHS [5], ULAP [20], FunIEGAN [10], Water-Net [15], Ucolor [8], MLLE [6], PGN and the Reference. Our method performs best on color correction and detail recovery. Especially in complex lighting conditions, our method preserves the most detail, with no artifacts, and the images are not too light or too dark. Figure 7: Visual comparison on the UGAN dataset. The comparison methods are arranged from left to right: Original images, IBLA [19], RGHS [5], ULAP [20], FunIEGAN [10], Water-Net [15], Ucolor [8], MLLE [6], PGN and the Reference. Our method performs best on detail recovery and color correction, and especially in the sample from UIEBD, our method preserves the most detail in complex lighting conditions.