# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Quantitative Comparison with Other State-of-the-Art Algorithms  
In this subsection, we first elucidate the testing of the proposed method and its competing methods on ten commonly used test images. AWGN, with the noise levels \(\sigma=\) 10, 20, 30, 40, 50, and 60, is added to these test images. The highest values obtained for each noise level are highlighted in bold in each of the tables. Table 1 lists the PSNR values for the test images _Boat_, _Couple_, _Man_, _Monarch_, and _Peppers_ for the noise level \(\sigma=\) 10, 30, and 50. It can be observed that the best PSNR values are obtained by our method for all these images. From the average PSNR values shown in Table 2, the following observations can be made. First, the proposed method surpasses the NCSR, PGPD, and BM3D by a substantial margin, and it also outperforms WNNM, DnCNN, TWSC, and FFDNet by an average of approximately 0.31\(\sim\)0.53 dB for a wide range of noise levels. Second, the proposed method has higher PSNR values than BM3D, NCSR, PGPD, WNNM, DnCNN, and TWSC, and it is only slightly inferior to the FFDNet when the noise level \(\sigma\) is set to 60. However, it gradually outperforms the FFDNet when \(\sigma<\) 60, and the proposed method performs exceptionally with regard to low-noise-level denoising. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Images** & **Noise Level** & **NCSR** & **WNNM** & **PGPD** & **BM3D** & **DnCNN** & **TWSC** & **FFDNet** & **Proposed** \\ \hline \multirow{3}{*}{_Boat_} & 10 & 34.62 & 34.77 & 34.44 & 34.65 & 34.65 & 34.69 & 34.68 & **35.90** \\  & 30 & 29.02 & 29.37 & 29.15 & 29.24 & 29.62 & 29.39 & 29.64 & **29.94** \\  & 50 & 26.62 & 26.99 & 26.91 & 26.80 & 27.35 & 26.99 & 27.41 & **27.59** \\ \hline \multirow{3}{*}{_Couple_} & 10 & 34.37 & 34.44 & 34.37 & 34.43 & 34.52 & 34.38 & 34.54 & **34.88** \\  & 30 & 28.61 & 29.03 & 28.84 & 28.94 & 29.35 & 29.02 & 29.43 & **29.67** \\  & 50 & 26.14 & 26.57 & 26.60 & 26.39 & 26.96 & 26.53 & 27.04 & **27.19** \\ \hline \multirow{3}{*}{_Man_} & 10 & 34.20 & 34.31 & 34.11 & 34.20 & 34.36 & 34.24 & 34.41 & **35.20** \\  & 30 & 28.77 & 28.99 & 28.79 & 28.88 & 29.32 & 28.95 & 29.33 & **29.65** \\  & 50 & 26.64 & 26.90 & 26.86 & 26.80 & 27.24 & 26.88 & 27.26 & **27.47** \\ \hline \multirow{3}{*}{_Monarch_} & 10 & 34.07 & 34.27 & 34.05 & 33.88 & 33.99 & 34.05 & 34.11 & **36.07** \\  & 30 & 28.29 & 28.79 & 28.47 & 28.22 & 28.97 & 28.57 & 29.00 & **29.08** \\ \cline{1-1}  & 50 & 25.69 & 26.22 & 26.02 & 25.64 & 26.65 & 26.16 & 26.65 & **26.77** \\ \hline \multirow{3}{*}{_Peppers_} & 10 & 34.90 & 35.05 & 34.82 & 34.98 & 34.94 & 34.88 & 34.85 & **36.40** \\  & 30 & 29.12 & 29.48 & 29.37 & 29.26 & 29.92 & 29.42 & 29.79 & **30.20** \\ \cline{1-1}  & 50 & 26.58 & 27.05 & 26.89 & 26.74 & 27.36 & 26.88 & 27.41 & **27.59** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of denoising results in terms of PSNR (dB) for five selected test images. Table 3 presents the average SSIM and FSIM values obtained for the competing methods evaluated on commonly used test images with noise level \(\sigma=10\), 20, 30, 40, 50 and 60. Table 4 lists the average VIF and IW-SSIM values obtained for the competing methods, for various denoising tasks carried out at six different noise levels. The proposed method outperforms TWSC, PGPD, and BM3D by a substantial margin. It demonstrates a noticeable denoising effect in low-noise-level denoising tasks; particularly, in terms of VIF, when the noise level is set to 40 and 50, our method surpasses the benchmark method BM3D by 0.085 and 0.060, respectively. Regarding images with a low noise level, many details are intact in the final image obtained using our method; thus, our method is able to eliminate the inaccuracy and uncertainty in denoised images obtained using the individual methods, thereby preserving the image details to the maximum extent. To further demonstrate the general applicability of our method, we employed 50 images from the BSD dataset.