# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 3 Results and Discussion
### Comparative Study  
In this experiment, we chose two evaluation methods, including visual evaluation and quantitative evaluation, to compare the specific effects of our model with other models. **Visual Evaluation**. In open water, due to the longest wave tension and fast propagation speed, red light, compared to other wavelengths, is absorbed more. Therefore, the underwater image appears blue or green. In order to clearly observe the effect of the image via MTUR-Net processing, we provide a comparison chart of the corresponding results obtained in different ways. Figure 4 shows that the output obtained by MTUR-Net had the best performance. Our solution repaired the chromatic aberration caused by different water areas, and one can see the details in the dark water and the texture of fish in the muddy water in the restored image. **Quantitative Evaluation**. We provide full-reference evaluation and non-reference evaluation to quantitatively analyze the performance of different methods. We conducted a full-reference evaluation using the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and frame per second (FPS). For the first two evaluation criteria, PSNR is a widely used objective evaluation index of image, which is an image quality evaluation based on the difference between corresponding pixels; SSIM compares local patterns of pixel intensities that have been normalized for luminance and contrast [26]. Although the real-world environmental situation may differ from the reference image, the results of a full-reference evaluation using the reference image can provide some feedback on the performance of different methods. A higher PSNR means that the result is less distorted, a higher SSIM implies that the result is more similar to the reference image structure, and a higher FPS means that the processing process is more efficient. In Table 1, we see that our method achieved the best PSNR and SSIM, while the FPS value was also ideal. Figure 4: Visual comparison of different images (from Test-R90) enhanced by state-of-the-art methods and our MTUR-Net. (**a**) Raw, (**b**) UDCP [2], (**c**) WaterNet [5], (**d**) Ucolor [9], (**e**) UGAN [11], (**f**) FUniE-GAN [6], (**g**) Ours, (**h**) Ground Truth. Then, we used UCIQE [27] and UIQM [28] for a non-reference evaluation. In principle, the higher UCIQE score, the better the balance of the standard deviation of the chroma, the better the contrast of the brightness, and the evenness of the saturation; for the higher UIQM score, the better the visual result, subjectively. As shown in Table 2, our proposed model obtained one of the best scores in UCIQE and UIQM. However, when we visually compared the images, we found that there were many small squares on UGAN's images, such as those shown in Figure 5, but the score was still very high, indicating that this evaluation standard still needs to be improved. At the same time, we tested the Subjectively-Annotated UIE benchmark Dataset (SAUD) [29], and its test data contains 100 unpaired images. Our experiment showed that our MTUR-Net still had one of the best results in UCIQE and UIQM, as shown in Table 3. Here, Ucolor's UIQM was higher than MTUR-Net, because Ucolor's setting is different from ours. The former network has two inputs in the input port, one is the original image, the other is the MT map artificially processed by the physical formula. However, the MTUR-Net generated the predicted MT map through its own training model at test time. Furthermore, our method still applies when testing on a dataset when a groundtruth MT map is unavailable, which is also one of its strengths.