# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 5 Experiment and Analysis
### Ablation Studies  
In our ablation experiments, the denoising model was trained for only 150,000 iterations. The tests were performed on BSD100 [38] and analyzed for a challenging AWGN + SPIN case (\(\sigma=25\), \(p_{sp}\)=30%). Tables 5-7 indicate the quality of improvements in performance achieved for various configurations. We then describe the impact of each component.  
Figure 8: Denoising results on image 105025 of the BSD100 dataset. (**a**) Original image. (**b**) Image corrupted with AWGN + RVIN (\(\sigma=15\), \(p_{r}=15\%\)). (**c**) MSNUNet.  
**Effect of input size.** We computed the FLOPs, PSNR, and SSIM for image sizes of \(128\times 128\), \(192\times 192\), and \(256\times 256\), respectively. As shown in Table 5, the PSNR gain becomes larger as the image size increases, and the FLOPs also increases. In this paper, we used patches with an image size of \(256\times 256\) as network inputs.  
**Effect of MSCAM.** To fully validate MSCAM, we replaced all MSCAM blocks in MSNUNet with Conv-based Resblocks [35], while the others were left unchanged. In addition, we removed DConv and CAB from MSCAM, resulting in two MSCAM variants, MSCAM-C and MSCAM-D.  
Table 6(d) demonstrates the exceptional progress achieved by our MSCAM approach, surpassing the standard Resblock (Table 6(a)) by an impressive 0.37 dB. Furthermore, the localization introduced by DConv enhances the robustness of MSCAM since its removal causes a decrease in PSNR (see Table 6(b)). In addition, including CAB produces a noteworthy enhancement of 0.23 dB, as revealed in Table 6(c). To further illustrate the effectiveness of MSCAM, Figure 9b,c shows the two cases of MSNUNet equipped with Resblock and MSCAM, respectively. It is evident that MSNUNet equipped with MSCAM restores additional details of the human within the image, which confirms that our method is able to retain more texture details.  
**Effect of MSU-Subnet.** Table 7(d) demonstrates that including a U-block design improves the denoising performance by 0.14 dB compared to a conventional U-network (see Table 7(b)). Furthermore, replacing the standard Resblock with MSCAM in the U-block  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Component** & **FLOPs (B)** & **PSNR (dB)** & **SSIM (\%)** \\ \hline \multirow{4}{*}{MSNUNet} & (a) Resblock & **95.83** & 27.78 & 77.94 \\  & (b) MSCAM-C & 110.32 & 28.03 & 79.12 \\ \cline{1-1}  & (c) MSCAM-D & 105.93 & 27.92 & 78.44 \\ \cline{1-1}  & (d) MSCAM & 116.11 & **28.15** & **79.67** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influence of MSCAM. The **best results** are marked in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Component** & **FLOPs (B)** & **PSNR (dB)** & **SSIM (\%)** \\ \hline \multirow{2}{*}{UNet} & (a) UNet with Resblock & **92.84** & 27.64 & _77.82_ \\  & (b) UNet with MSCAM & 108.46 & 28.01 & 79.10 \\ \hline \multirow{2}{*}{MSNUNet} & (c) U-block with Resblock & 100.85 & 27.79 & 78.36 \\  & (d) U-block with MSCAM & 116.11 & **28.15** & **79.66** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Influence of MSU-Subnet. The **best results** are marked in bold.  
Figure 9: Denoising results on image 14037 of the BSD100 dataset. (**a**) Original image. (**b**) MSNUNet equipped with Resblock. (**c**) MSNUNet equipped with MSCAM.  
enhances the aggregation of multi-scale features, as shown by a decrease in PSNR upon its removal. Overall, MSNUNet contributes a substantial gain of 0.51 dB over the baseline (see Table 7(a)).