# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis  
### Real Image Deblurring  
To evaluate the performance of IDR\({}^{2}\)ENet, traditional methods such as those proposed by [PERSON] et al. [6], [PERSON] et al. [21], and [PERSON] et al. [38], as well as deep-learning-based methods such as SRN [9], SVRNN [39], DeepDeblur [8], DeblurGAN [10], DMPHN [40], DeblurGAN-v2 [16], DBGAN [31], MIMO-Unet [41], MIMO-Unet+ [41], MPRNet [42], and Lightweight MIMO-WNet [43], are introduced in this paper for comparison. We first tested the objective metric PSNR/SSIM results of each deblurring method on the real blur datasets RealBlur-J and RealBlur-R, which are shown in Table 1, respectively. As seen in Table 1, the IDR\({}^{2}\)ENet approach proposed in this paper obtains superior results on both the RealBlur-J and RealBlur-R datasets. The PSNR and SSIM of traditional methods lag behind those of most of the deep-learning-based methods, the results of which are lower on both the RealBlur-J and RealBlur-R datasets, which indicates that the traditional-based method models deblurring as a specific mathematical process that cannot cope with the complex degradation in real blurred images and does not work well. When compared to deep learning-based methods, IDR\({}^{2}\)ENet shows some improvement in effectiveness, such as an objective metric increase of 0.11 dB/0.003 in the RealBlur-J dataset compared to the newer MPRNet. Furthermore, Figure 8A,B show the deblurred visual performances of different methods on two real blurred images from the Reblur-J dataset. In Figure 8A, it can be seen that the blurred image suffers from severe blur degradation. The image reconstructed by DeblurGAN-v2 achieves some deblurring effect. However, compared with the results of IDR\({}^{2}\)ENet, the reblurred image recovered by DeblurGAN-v2 still retains blur artifacts and a purple-red artifact on the wall from the poster on the left side, and the reblurred image of IDR\({}^{2}\)ENet is clearer and sharper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**RealBlur-J**} & \multicolumn{2}{c}{**RealBlur-R**} \\ \cline{3-6}  & & **PSNR (dB)** & **SSIM** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{4}{*}{Traditional} & [PERSON] et al. [6] & 27.14 & 0.830 & 34.46 & 0.937 \\ \cline{2-6}  & [PERSON] et al. [21] & 26.41 & 0.803 & 33.67 & 0.916 \\ \cline{2-6}  & [PERSON] et al. [38] & 27.22 & 0.790 & 34.01 & 0.917 \\ \hline \multirow{6}{*}{Deep-Learning-Based} & SRN [9] & 28.56 & 0.867 & 35.66 & 0.947 \\ \cline{2-6}  & SVRNN [39] & 27.80 & 0.847 & 35.48 & 0.945 \\ \cline{2-6}  & DeepDeblur [8] & 27.87 & 0.827 & 32.51 & 0.841 \\ \cline{2-6}  & DeblurGAN [10] & 27.97 & 0.834 & 33.79 & 0.903 \\ \cline{2-6}  & DMPHN [40] & 28.42 & 0.860 & 35.70 & 0.948 \\ \cline{2-6}  & DeblurGAN-V2 [16] & 28.70 & 0.867 & 35.26 & 0.944 \\ \cline{2-6}  & DBGAN [31] & 24.93 & 0.745 & 33.78 & 0.909 \\ \cline{2-6}  & MIMO-Unet [41] & 27.76 & 0.836 & 35.47 & 0.946 \\ \cline{2-6}  & MIMO-Unet+ [41] & 27.63 & 0.837 & 35.54 & 0.947 \\ \cline{2-6}  & MPRNet [42] & 28.70 & 0.873 & 35.99 & 0.952 \\ \cline{2-6}  & Lightweight & \multirow{2}{*}{28.52} & \multirow{2}{*}{0.865} & \multirow{2}{*}{35.76} & \multirow{2}{*}{0.950} \\ \cline{2-6}  & MIMO-WNet [43] & & & & \\ \cline{2-6}  & **IDR\({}^{2}\)ENet (Ours)** & **28.81** & **0.876** & **35.96** & **0.952** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of PSNR/SSIM for different methods on RealBlur-J and RealBlur-R.  
Figure 8: **Deblurring performances of various methods on the RealBlur-J dataset. ** From left to right, top to bottom: (**a**) blurred image, (**b**) DeblurGAN-v2 [16], (**c**) DMPHN [40], (**d**) MIMO-UNet+ [41], (**e**) MPRNet [42], (**f**) IDR2 ENet (**Ours**). (**A**) Deblurring performance: Image-1. (**B**) Deblurring performance: Image-2. From the enlarged font blocks, the deblurred results of DeblurGAN-v2 are sharper, but still have slight artifacts at the edges of the font, while the results of IDR\({}^{2}\)ENet do not.