# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Quantitative Comparison with Other State-of-the-Art Algorithms
In addition, we calculated the metrics VIF and IW-SSIM to further assess the performance of our method. From Table 6, it is clear that the result obtained using the proposed boosting method is more pleasing than the denoised image obtained using either DnCNN or NCSR. A majority of the best metric values are also achieved by our method. In addition to considering traditional datasets, we also evaluated the performance of our method on synthetic images. The PSNR results are reported in Table 7. The experiments conducted at low (\(\sigma=10\)) and high (\(\sigma=50\)) noise levels show that the proposed method outperforms all of the 7 comparable methods. Moreover, in terms of the average PSNR results, our method is the best among all the competitors. The proposed boosting method is able to boost the PSNR value by an approximate average of 0.68 and 0.17 compared to NCSR and DnCNN, respectively. The experimental results demonstrate that the proposed method can achieve a state-of-the-art denoising performance in different datasets. Thus, our method possesses a high generalizability and applicability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\sigma=10\) & \(\sigma=20\) & \(\sigma=30\) & \(\sigma=40\) & \(\sigma=50\) & \(\sigma=60\) \\ \hline NCSR & 0.605/0.980 & 0.447/0.945 & 0.363/0.909 & 0.325/0.871 & 0.292/0.837 & **0.264** / 0.803 \\ WNNM & 0.606/0.981 & 0.438/0.948 & 0.356/0.914 & 0.304/0.879 & 0.274/0.848 & 0.247/0.816 \\ PGPD & 0.594/0.979 & 0.430/0.945 & 0.351/0.910 & 0.303/0.876 & 0.271/0.842 & 0.233/0.817 \\ BM3D & 0.602/0.981 & 0.433/0.947 & 0.346/0.912 & 0.285/0.877 & 0.270/0.844 & 0.241/0.815 \\ DnCNN & 0.602/0.982 & 0.437/0.953 & 0.353/0.922 & 0.303/0.890 & 0.268/0.860 & 0.187/0.830 \\ TWSC & 0.558/0.981 & 0.371/0.949 & 0.278/0.914 & 0.222/0.878 & 0.184/0.843 & 0.157/0.810 \\ FFDNet & 0.603/0.982 & 0.441/0.954 & 0.361/0.924 & 0.314/0.894 & 0.282/0.864 & 0.257/0.837 \\ proposed & **0.659**/**0.985** & **0.497**/**0.958** & **0.412**/**0.929** & **0.365**/**0.898** & **0.324**/**0.868** & 0.245/**0.844** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average VIF/IW-SSIM values of the competing methods obtained for 50 images selected from the BSD dataset with noise level \(\sigma=\) 10, 20, 30, 40, 50 and 60. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **NCSR** & **WNNM** & **PGPD** & **BM3D** & **DnCNN** & **TWSC** & **FFDNet** & **Proposed** \\ \hline \(\sigma=10\) & 36.14 & 36.33 & 36.03 & 36.37 & 36.37 & 36.30 & 36.47 & **36.52** \\ \(\sigma=30\) & 30.59 & 30.91 & 30.73 & 30.72 & 31.23 & 30.92 & **31.38** & 31.02 \\ \(\sigma=50\) & 28.21 & 28.53 & 28.45 & 28.30 & 28.88 & 28.44 & 29.08 & **29.45** \\ Average & 31.65 & 31.92 & 31.74 & 31.80 & 32.16 & 31.89 & 32.31 & **32.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average PSNRs (dB) of the competing methods for images in the ESPL synthetic image database with noise level \(\sigma=\) 10, 30 and 50. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **NCSR** & **WNNM** & **PGPD** & **BM3D** & **DnCNN** & **TWSC** & **FFDNet** & **Proposed** \\ \hline \(\sigma=10\) & 33.42 & 33.53 & 33.31 & 33.42 & 33.48 & 33.54 & 33.61 & **34.75** \\ \(\sigma=20\) & 29.56 & 29.72 & 29.51 & 29.54 & 29.70 & 30.00 & 30.02 & **30.30** \\ \(\sigma=30\) & 27.60 & 27.80 & 27.58 & 27.59 & 27.74 & 28.12 & 28.15 & **28.38** \\ \(\sigma=40\) & 26.26 & 26.53 & 26.36 & 26.25 & 26.46 & 26.88 & 26.92 & **27.08** \\ \(\sigma=50\) & 25.35 & 25.63 & 25.47 & 25.39 & 25.52 & 25.98 & 26.03 & **26.16** \\ \(\sigma=60\) & 24.60 & 24.91 & 24.80 & 24.72 & 24.79 & 24.86 & 25.34 & **25.37** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average PSNRs (dB) of the competing methods obtained for 50 images selected from the BSD dataset with noise level \(\sigma=\) 10, 20, 30, 40, 50 and 60.