# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 5 Ablation Study  
We performed ablation studies to explore the effectiveness of the proposed modules. For the ablation studies, we used variant models derived from a small version of our FreANet. All these variant models were evaluated on the SOTS-Indoor dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LOLv1**[45]} & \multicolumn{2}{c}{**LOLv2-Real**[46]} & \multicolumn{2}{c}{**LOLv2-Syn**[46]} & \multirow{2}{*}{**\#Params**} \\ \cline{2-2} \cline{6-7}  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **(M)** \\ \hline DRBN [57] & 20.13 & 0.830 & 20.29 & 0.831 & 23.22 & 0.927 & 5.27 \\ Restormer [7] & 22.43 & 0.823 & 19.94 & 0.827 & 21.41 & 0.830 & 26.13 \\ MIRNet [21] & 24.14 & 0.830 & 20.02 & 0.820 & 21.94 & 0.876 & 31.76 \\ UHDFour [29] & 23.09 & **0.870** & **21.78** & **0.870** & - & - & 28.52 \\ SNR-Net [58] & **24.61** & 0.842 & 21.48 & 0.849 & 24.14 & 0.928 & 4.01 \\
**FreANet** & 23.60 & 0.836 & **21.78** & 0.860 & **25.91** & **0.939** & 2.45 \\ \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 6: Low-light image enhancement comparisons on the LOLv1 [45] and LOLv2 [46] test sets.  
Figure 5: Low-light image enhancement results on the LOLv1 [45] test set. FreANet effectively enhances the visual quality of the images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **OFSD** & **HCD** & **FocalNet** & **FSNet** & **OKNet** & **FreANet** \\  & [44] & [55] & [11] & [50] & [19] & **(Ours)** \\ \hline PSNR\(\uparrow\) & 21.32 & 23.43 & 25.35 & 26.30 & 27.92 & **28.76** \\ SSIM\(\uparrow\) & 0.804 & 0.953 & 0.969 & 0.976 & **0.979** & 0.978 \\ \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 5: Nighttime dehazing comparisons on the NHR [44] test set.  
Table 7 presents the quantitative results for the variant models. First, the initial model (a) results in a PSNR of 30.75 dB. The model (b) utilizing only the RDB module achieves a PSNR of 36.29 dB, while the variant model (c) with MPA yields a PSNR gain of 0.77 dB, with a slight increase of 0.19 M parameters. We investigated the components of the MDFE module, including the spatial and frequency branches. The model (d) equipped only with the spatial branch achieves a 33.36 dB PSNR. In the case of the frequency branch, model (e) causes a performance degradation due to insufficient spatial texture information. However, when combined with the spatial branch, model (f) leads to a significant gain of 2.47 dB over model (d). Finally, the progressive addition of MPA and RDB modules produces improved results. Our full model (i) outperforms other variant models, achieving an 8.79 dB boost over the initial model (a).  
Visual results of each component.To analyze the impact of individual components