# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 4 Experiments and Analysis
### Experimental Results  
(a) Quantitative Analysis: We adopted the peak-signal-to-noise-ratio (PSNR) and structural similarity (SSIM) to evaluate the image quality. Meanwhile, the parameters indicate that a lightweight and effective model is the current research trend. Considering our model is a kind of CNN-transformer combination structure, we compared our model with CNN-based models [2; 7; 8; 10; 11; 12; 13; 14; 15; 19] and a transformer-based model [18]. Table 1 shows a comparison with the advanced models based on the GoPro [7] dataset and the RealBlur [31] dataset.  
From Table 1, we can see that our model outperforms SSIM results in the most deblurring models. Notably, we only trained our model on the GoPro dataset and then directly used it to test the RealBlur dataset, proving that our model has a good generalization ability. The PSNR results are also better than most of the CNN-based models. Compared with the transformer-based model Restomer [18], our model achieves better SSIM results with smaller model parameters. Moreover, some CNN-based models have small parameters, but the evaluation results are poor, and some achieve good results with larger model sizes. In contrast, our model balances performance and parameters; it has comparable evaluation results and relatively small model parameters.  
(b) Qualitative Analysis: Figures 7 and 8 show the visual examples of comparison on the GoPro test set with [2; 8; 12; 17; 18] and the RealBlur test set with [7; 8; 10; 12; 18]. We  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**GoPro**} & \multicolumn{2}{c}{**RealBlur**} & \multirow{2}{*}{**Params**} \\  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & & **(M)** \\ \hline \multirow{8}{*}{CNN-based models} & [PERSON] et al. [7] & 29.08 & 0.914 & 27.87 & 0.827 & 11.7 \\  & [PERSON] et al. [11] & 29.19 & 0.931 & 27.80 & 0.847 & 9.2 \\  & DeblurGAN-V2 [10] & 29.55 & 0.934 & 28.70 & 0.866 & 60.9 \\  & SRN [12] & 30.26 & 0.934 & 28.56 & 0.867 & 6.8 \\  & DBGAN [15] & 31.10 & 0.942 & 24.93 & 0.745 & 11.6 \\  & MT-RNN [14] & 31.15 & 0.945 & 28.44 & 0.862 & 2.6 \\  & DMPHN [13] & 31.20 & 0.940 & 28.42 & 0.860 & 21.7 \\  & BANet [17] & 32.44 & 0.957 & - & - & 85.65 \\  & SDWNet [16] & 31.26 & 0.966 & 28.61 & 0.867 & 7.2 \\  & FMD-cGAN [19] & 28.33 & 0.962 & - & - & 1.98 \\  & MIMO-UNet+ [8] & 32.45 & 0.957 & 27.63 & 0.837 & 16.1 \\  & MPRNet [2] & 32.66 & 0.959 & 28.70 & 0.873 & 20.1 \\ \hline Transformer-based model & Restomer [18] & 32.92 & 0.961 & 28.96 & 0.879 & 26.12 \\ \hline CNN-Transformer & Our model & 32.68 & 0.962 & 28.73 & 0.881 & 15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deblurring results of the advanced deblurring models, our model is trained on the GoPro [7] dataset and directly evaluates the RealBlur [31] test dataset.  
present local details to demonstrate the restoration results of each model. Some previous CNN-based models cannot restore the font counters well and the transformer-based Restomer [18] exhibited good performance. In Table 1, we can see that Restomer [18] has the best PSNR results, and our model outperforms SSIM results in most deblurring models. Compared with the previous models, our model can effectively restore the detail texture, and perform better than Restomer [18] in some scenes, For instance, in the first example in Figure 7 and the last example in Figure 8, our model present sharper results.