# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 4 Experiments
### Motion Deblurring Results  
We evaluated deblurring models on the GoPro [39] and HIDE [40] datasets and report the quantitative results in Table 3. Our method achieves a PSNR of 33.21 dB on the GoPro, using fewer parameters compared to the transformer-based Stripformer [52]. FreANet also demonstrates remarkable generalization performance on HIDE. We visualize the deblurred results in Figure 3, where it can be seen that our FreANet restores images more sharply and distinctly than its competitors.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Hdoor Scenes**} & \multicolumn{3}{c}{**Outdoor Scenes**} & \multicolumn{3}{c}{**Combined**} & \multicolumn{3}{c}{**\#Params**} \\ \cline{2-13}
**Methods** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MAE\(\downarrow\)** & **LPIPS\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MAE\(\downarrow\)** & **LPIPS\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MAE\(\downarrow\)** & **LPIPS\(\downarrow\)** & **(M)** \\ \hline IFAN [49] & 28.11 & 0.861 & 0.026 & 0.179 & 22.76 & 0.720 & 0.052 & 0.254 & 25.37 & 0.789 & 0.039 & 0.217 & 10.47 \\ Restormer [7] & 28.87 & **0.882** & 0.025 & 0.145 & 23.24 & 0.743 & 0.050 & 0.209 & 25.98 & 0.811 & 0.038 & 0.178 & 26.13 \\ SFNet [10] & 29.16 & 0.878 & **0.023** & 0.168 & 23.45 & 0.747 & **0.049** & 0.244 & 26.23 & 0.811 & 0.037 & 0.207 & 13.27 \\ FocalNet [11] & 29.10 & 0.876 & 0.024 & 0.173 & 23.41 & 0.743 & **0.049** & 0.246 & 26.18 & 0.808 & 0.037 & 0.210 & 12.82 \\ IRNeXt [18] & 29.22 & 0.879 & 0.024 & 0.167 & 25.53 & 0.752 & **0.049** & 0.244 & 26.30 & 0.814 & 0.037 & 0.206 & 14.75 \\ OKNet [19] & 28.99 & 0.877 & 0.024 & 0.169 & 23.51 & 0.751 & **0.049** & 0.241 & 26.18 & 0.812 & 0.037 & 0.206 & 14.02 \\ FSNet [50] & 29.14 & 0.878 & 0.024 & 0.166 & 23.45 & 0.747 & 0.050 & 0.246 & 26.22 & 0.811 & 0.037 & 0.207 & 13.28 \\ DSANet [51] & 29.27 & 0.881 & 0.024 & 0.158 & 23.50 & 0.749 & **0.049** & 0.231 & 26.31 & 0.813 & **0.036** & 0.195 & 13.16 \\
**FEANet** & **29.32** & 0.881 & **0.023** & **0.131** & **23.55** & **0.757** & **0.049** & **0.195** & **26.36** & **0.817** & **0.036** & **0.164** & 12.74 \\ \hline \hline \end{tabular} The best and second-best scores are highlighted in **bold** and underlined, respectively. \(\uparrow\) indicates that a higher score is better, whereas \(\downarrow\) indicates that a lower score is better.  
\end{table}
Table 2: Defocus deblurring comparisons on the DPDD [37] test set.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**GoPro [39]**} & \multicolumn{2}{c}{**HIDE [40]**} & \multicolumn{1}{c}{**\#Params**} \\ \cline{2-5}
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **(M)** \\ \hline MPRNet [3] & 32.66 & 0.959 & 30.96 & 0.939 & 20.1 \\ HINet [5] & 32.71 & 0.959 & 30.32 & 0.932 & 20.1 \\ Restormer [7] & 32.92 & 0.961 & **31.22** & 0.942 & 26.13 \\ Uformer-B [53] & 33.06 & **0.967** & 30.90 & **0.953** & 50.88 \\ Stripformer [52] & 33.08 & 0.962 & 31.03 & 0.940 & 20 \\ IRNeXt [18] & 33.16 & 0.962 & - & - & 14.75 \\
**FreANet** & **33.21** & 0.962 & 31.11 & 0.937 & 12.74 \\ \hline \hline \end{tabular} The best and second-best scores are highlighted in **bold** and underlined, respectively. \(\uparrow\) indicates that a higher score is better.  
\end{table}
Table 3: Motion deblurring comparisons on the GoPro [39] and HIDE [40] test sets.  
Figure 2: Defocus deblurring results on the DPDD [37] test set. These results demonstrate that our method effectively restores the halo blur effect.