# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Qualitative Analysis  
For the visual comparisons, we first selected several underwater images from the UIEB, EUVP, and RUIE datasets, which consist of three categories: greenish images, bluish images, and turbid images. The results of the different methods and the corresponding reference images (ground truth) are shown in Figures 7-9.  
**Visual comparisons on the UIEB dataset:** Figure 7 presents the enhancement results of the proposed method and other methods on the UIEB underwater dataset for various test images. Traditional methods based on physical models, such as DCP and UDCP, show limited effectiveness in correcting blue-green color bias. Several methods (DeepSear, Funie-GAN, and MLFcGAN) tend to produce red casts in turbid images. The proposed method effectively removes both blur and image color bias, resulting in clear texture details, sharp edge colors, and high contrast. The produced image is closest to the original clear image for all three kinds of underwater images (greenish images, bluish images, and turbid images).  
**Visual comparisons on the EUVP dataset:** Figure 8 shows the enhancement results on the EUVP underwater dataset. Most images in the EUVP dataset are greenish or bluish. Traditional methods, such as DCP and UDCP, exhibit less effectiveness in correcting color bias, while newer methods, such as DeepSesar, Funie-GAN, MLFcGAN, and SCNET, achieve relatively better results. However, most of the methods fail to restore those images with heavy  
Figure 7: Visual comparison of the enhancement results obtained from the UIEB dataset. From left to right are the raw underwater images and the DCP, UDCP, DeepSesar, FuniGan, MLFcGAN, UWCNN-SD, USUIR, and SCNET results, as well as our DeMBFF-Net, and the reference image (recognized as the ground truth).  
color casts (the third and sixth rows in Figure 8). The proposed DeMBFF-Net outperforms the others by effectively removing blurring and color bias, resulting in clear textural details. The overall color recovery is closest to that of the original clear image.  
Figure 8: Visual comparison of enhancement results sampled from the EUVP dataset. From left to right are the raw underwater images and the DCP, UDCP, DeepSesr, FuniGan, MLFcGaN, UWCNN-SD, USUIR, and SCNET results, as well as our DeMBFF-Net, and the reference image (recognized as the ground truth).  
Figure 9: Visual comparison of enhancement results sampled from the RUIE dataset. From left to right are the raw underwater images and the DCP, UDCP, DeepSesr, FuniGan, MLFcGaN, UWCNN-SD, USUIR, SCNET, and our DeMBFF-Net results.  
**Visual comparisons on the RUIE dataset:** Figure 9 illustrates the enhancement results on the RUIE real underwater dataset. Traditional methods, such as DCP and UDCP, show poor image recovery. For underwater images with only mild blue color bias (the first and second rows), the depth-based methods achieve better results. For underwater images with only green color bias (the third and fourth rows in Figure 9), only the UWCNN-SD and our method effectively remove the color bias. For underwater images with both slight color bias and blur (the fifth and sixth rows), only the SCNET and DeMBFF-Net methods remove both blur and color bias. The results show that our method has higher contrast, better clarity, and better subjective effects than the other methods.