## 4 Experiments
### Gray Image Denoising
As shown in Table 3, the performance of the proposed method is slightly lower than that of our previous methods. The main reason for this is that the proposed method employs only FFDNet to generate multiple intermediate denoised images. The diversity of the intermediate denoised images is not as high as that of the intermediate denoised images generated in our previous studies. The two methods we previously proposed achieved very good performance on synthetic noisy image denoising. However, it should be noted that the denoiser proposed in [32] did not improve the performance of real-world noisy images, and the method proposed in [31] cannot be used for real-world image denoising. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(\sigma\) & **Image** & **BM3D** & **WNNM** & **DnCNN** & **FFDNet** & **DAGL** & **DeamNet** & **DRUNet** & **IRCNN** & **SwinIR** & **DIP** & **N2V** & **Ours** \\ \hline
15 & C.man & 31.90 & 32.19 & 32.14 & 32.42 & 32.69 & 32.76 & **32.91** & 32.53 & **33.04** & 30.90 & 29.09 & 32.75 \\  & House & 34.92 & 35.14 & 34.96 & 35.01 & 35.76 & 35.68 & **35.83** & 34.88 & **35.91** & 34.04 & 34.22 & 35.55 \\  & Peppers & 32.71 & 32.98 & 33.09 & 33.10 & 33.41 & 33.42 & 33.56 & 33.21 & **33.63** & 31.85 & 31.39 & **33.89** \\  & Starfish & 31.12 & 31.82 & 31.92 & 32.02 & **32.61** & 32.50 & 32.44 & 31.96 & 32.57 & 30.98 & 30.62 & **33.04** \\  & Monarch & 31.95 & 32.71 & 33.08 & 32.77 & 33.48 & 33.51 & **33.61** & 32.98 & **33.68** & 31.52 & 30.84 & 33.51 \\  & Airplane & 31.10 & 31.41 & 31.54 & 31.58 & 31.95 & 31.90 & 31.99 & 31.66 & **32.08** & 30.41 & 27.99 & **32.57** \\  & Parrot & 31.31 & 31.64 & 31.64 & 31.77 & 31.96 & 32.01 & 32.13 & 31.88 & **32.16** & 30.51 & 28.35 & **32.29** \\  & Lena & 34.23 & 34.37 & 34.52 & 34.63 & 34.84 & 34.86 & 34.93 & 34.50 & **34.97** & 33.37 & 33.69 & **35.28** \\  & Barbara & 33.04 & 33.59 & 32.03 & 32.50 & **33.73** & 33.16 & 33.44 & 32.41 & **33.98** & 29.62 & 31.15 & 32.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR (dB) values of different methods on 12 gray images in the Set12 dataset with noise level \(\sigma\) at 15, 25, and 50. The best two performances are highlighted in boldface. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Set12**} & \multicolumn{4}{c}{**BSD68**} \\ \cline{2-9}  & **15** & **25** & **50** & **Avg.** & **15** & **25** & **50** & **Avg.** \\ \hline BM3D & 32.36 & 29.96 & 26.70 & 29.67 & 31.15 & 28.57 & 25.58 & 28.43 \\ WNNM & 32.71 & 30.26 & 26.88 & 31.45 & 31.35 & 28.80 & 25.81 & 28.65 \\ DnCNN & 32.67 & 30.35 & 27.18 & 30.07 & 31.62 & 29.14 & 26.18 & 28.98 \\ FFDNet & 32.75 & 30.43 & 27.32 & 30.17 & 31.65 & 29.17 & 26.24 & 29.02 \\ DAGL & 33.19 & 30.86 & 27.75 & 30.60 & 31.81 & 29.33 & 26.37 & 29.17 \\ DeamNet & 33.13 & 30.78 & 27.72 & 30.55 & 31.83 & 29.35 & 26.42 & 29.20 \\ DRUNet & 33.25 & 30.94 & 27.90 & 30.69 & 31.86 & 29.39 & **26.49** & 29.25 \\ IRCNN & 32.76 & 30.37 & 27.12 & 30.08 & 31.65 & 29.13 & 26.14 & 28.97 \\ SwinIR & **33.36** & 31.01 & **27.91** & **30.76** & 31.91 & 29.41 & 26.47 & 29.26 \\ DIP & 31.31 & 28.92 & 25.57 & 28.60 & 30.05 & 27.47 & 24.25 & 27.26 \\ N2V & 30.86 & 29.46 & 25.83 & 28.71 & 29.38 & 27.81 & 25.37 & 27.52 \\ Yu2022 & **34.05** & **31.58** & **28.11** & **31.24** & **32.92** & **30.17** & **26.92** & **30.00** \\ Xu2022 & **33.27** & **31.13** & **28.10** & **30.83** & **32.24** & **30.00** & **26.48** & **29.57** \\ Ours & **33.36** & **31.02** & 27.71 & 30.70 & **32.36** & **29.66** & 26.30 & **29.44** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the averaged PSNR (dB) of different methods on the Set12 and BSD68 datasets. The best three performances are highlighted in boldface.