# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 4 Experimental Results and Discussion
### Evaluation on Real-World Super Resolution  
The main challenge of super-resolution is a real-world application. Thus, we evaluated the effectiveness of the proposed IDFlow on DPED-Test in which all the images are blurry or noisy taken from different real scenarios, much more challenging for SR. We used standard IDFlow to generate realistic noisy LR training images based on the DF2K dataset. Some examples are shown in Figure 7. We then used the synthetic training dataset to re-train ESRGAN [4] for comparison for a better perceptual reconstruction. The trained model was named as IDFlow-GAN. The state-of-the-art SR methods being compared include K-ZSSR [11], DSGAN [9], SRFlow [20], the standard RRDBNet [4], and ESRGAN [4]. We also trained an ESRGAN with a RealSR-Train dataset added with synthetic noises generated by IDFlow for comparison, denoted as noisyRealSR.  
Figure 6: Visual comparisons of SR models on images “Nikon040” and “Nikon017” with a scale factor of 4\(\times\). Local regions of the results are cropped and zoomed for a better visual comparison.  
Figure 7: Examples of realistic noisy image patches generated by IDFlow. The images in (**a**) and (**b**) have similarly noisy visual patterns.  
Since there is no ground truth of testing LR images, we first conducted an evaluation on the SR images generated by different SR methods with no-reference IQA metrics. The results are reported in Table 4. We then made a visual comparison by presenting the local image regions of different SR results. As shown in Figure 8, SRFlow, ESRGAN, and RRDBNet cannot eliminate noises inside the images since they are all trained on clean Bicubic datasets, resulting in visible artifacts and abnormal textures. In contrast, the SR results of IDFlow-GAN have richer textures and clearer patterns, and there are fewer artifacts and less noise than the results of DSGAN as well as K-ZSSR. noisyRealSR is able to restore the sharper image edges and maintain cleaner patterns than DSGAN. This demonstrates that IDFlow realizes effective real-world noise modeling, so that noisyRealSR can eliminate the visible noises in testing images. However, it fails to generate clear tiny textures such as the tree branches shown in the first row of Figure 8. The main reason is that the number and diversity of the RealSR dataset is limited, causing a lack of enough high-frequency information to improve the SR performance on image details. This also proves from the opposite side that IDFlow alleviates the current deficiencies in existing real-world SR datasets. A more robust SR model can be trained. Therefore, IDFlow-GAN achieves better SR performance than noisyRealSR on real-world images.  
Both qualitative and quantitative experiments indicate that IDFlow can accurately model image degradation and generate realistic noisy images. Benefiting from IDFlow, the performance of the existing SR methods in real scenarios can be significantly promoted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SR Method** & **NIQE \(\downarrow\)** & **BRISQUE \(\downarrow\)** & **PIQE \(\downarrow\)** \\ \hline RRDBNet & 7.01 & 55.99 & 77.54 \\ SRFlow & 4.20 & 21.76 & 17.71 \\ ESRGAN & 4.02 & 19.63 & 16.09 \\ K-ZSSR & 8.15 & 58.73 & 80.67 \\ DSGAN & 3.5 & 11.34 & 12.06 \\ \hline noisyRealSR & 5.39 & 32.09 & 42.85 \\ IDFlow-GAN & 3.76 & 7.95 & 14.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with different state-of-the-art SR methods on the DPED-Test dataset with a scale of 4\(\times\).  
Figure 8: Visual results of different SR methods on DPED-Test real-world images with a scale of 4\(\times\). Local regions of the results are cropped and zoomed for a better visual comparison.