# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.3 Evaluations on the BSD Dataset  
To further validate our method's generalization capability, we evaluate its performance on the BSD dataset. This dataset features diverse blur characteristics through different exposure-time configurations. Longer exposure times (3 ms-24 ms) lead to more severe blur, posing greater challenges for deblurring algorithms. As shown in Table 3, our method achieves superior performance across all exposure configurations.  
Figure 6 demonstrates our deblurring results under three typical motion patterns. In static camera scenes, we successfully restore sharp contours of blurred motorcyclists. The original clear background details are also well preserved. For scenes with aligned camera and object motion, our method shows excellent performance. It accurately reconstructs the car front's edge structure and precisely recovers the front grille's texture. In scenes with opposing camera and object motion, our method effectively recovers vehicle body text. The restored text maintains high clarity without introducing artifacts. These results demonstrate our method's robustness and superiority in handling real-world motion blur patterns.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**DVD**[36]} & \multirow{2}{*}{**SRN**[29]} & \multirow{2}{*}{**DVDSFE**[45]} & \multirow{2}{*}{**ESTRNN**} & \multirow{2}{*}{**CDVD-**********} \\  & & & & [45] & [39] & **TSP**[7] \\ \hline PSNR & 27.31 & 30.29 & 31.01 & 31.07 & 31.67 & 32.73 \\ SSIM & 0.8255 & 0.9014 & 0.9130 & 0.9023 & 0.9279 & 0.9366 \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**STDAN**[9]} & \multirow{2}{*}{**RNN-**} & \multirow{2}{*}{**RST**[12]} & \multirow{2}{*}{**EGST**[12]} & \multirow{2}{*}{**LightViD**[47]} & \multirow{2}{*}{**STCT**[48]} & \multirow{2}{*}{**Ours**} \\  & & & & & \\ \hline PSNR & 32.62 & 33.32 & 32.90 & 32.73 & 32.97 & **34.87** \\ SSIM & 0.9375 & 0.9627 & 0.9610 & 0.9410 & 0.9406 & **0.9729** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparisons on the GoPro dataset. Bold indicates the best performance and underline indicates the second best performance.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**1 ms–8 ms**} & \multicolumn{2}{c}{**2 ms–16 ms**} & \multicolumn{2}{c}{**3 ms–24 ms**} \\  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline SRN [29] & 31.84 & 0.917 & 29.95 & 0.891 & 28.92 & 0.882 \\ STFAN [49] & 32.78 & 0.922 & 32.19 & 0.919 & 29.47 & 0.872 \\ CDVD-TSP [7] & 33.54 & 0.942 & 32.16 & 0.926 & 31.58 & 0.926 \\ ESTRNN [39] & 33.36 & 0.937 & 31.95 & 0.925 & 31.39 & 0.926 \\ STDAN [9] & 34.32 & 0.946 & 33.27 & 0.942 & 32.83 & 0.944 \\ FGST [12] & 34.60 & 0.969 & 33.62 & 0.962 & 33.16 & 0.959 \\ Ours & **34.85** & **0.971** & **33.91** & **0.969** & **33.34** & **0.966** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparisons on the BSD dataset. Bold indicates the best performance and underline indicates the second best performance.  
Figure 6: Comparison of visual results on the BSD dataset (3 ms–24 ms). Zoom in for better visualization.